Analytic Methods for Spatio-temporal Data in a Nature-inspired Data Model
Transcript of Analytic Methods for Spatio-temporal Data in a Nature-inspired Data Model
International Review on Computers and Software (I.RE.CO.S.), Vol. 9, N. 3ISSN 1828-6003 March 2014
Manuscript received and revised February 2014, accepted March 2014 Copyright © 2014 Praise Worthy Prize S.r.l. - All rights reserved
547
Analytic Methods for Spatio-Temporal Datain a Nature-Inspired Data Model
A. Madraky, Z. A. Othman, A. R. Hamdan
Abstract – We are surrounded by information and much of it needs to be stored and analysed.Data analysis would be easier if the data storage structure were closer to that of a natural datastructure. Many storage structures and related methods have been proposed in recent years due tothe importance of understanding spatio-temporal information associated with a particular placeand time. In this paper, some of the most important analytic methods for spatio-temporal data areconsidered and categorized in terms of their algorithms. We also describe the difficulties ofknowledge representation when dealing with spatio-temporal data. In addition, three of theanalytic functions of theHair-oriented Data Model are defined, which is a nature-inspiredsolution. These analytic functions are implemented in Oracle and tested on climate change data asa case study. The main objectives of this research are to propose a model to achieve betterknowledge representation, provide the capability to expand queries through additional analyticalattributes and reduce redundancy, and thereby obtain better integrity and consistency in spatio-temporal databases. Copyright © 2014 Praise Worthy Prize S.r.l. - All rights reserved.
Keywords: Data Engineering, Data Mining, Spatio-Temporal Data Models, Analytic Methods,Hair-Oriented Data Model
I. Introduction
Spatio-temporal data is an important part of theinformation around us. Climate change predictionsystems, agricultural information, traffic status and urbantransportation, population statistics and demographics allutilize this type of data. Spatio-temporal processing andanalysis are essential for decision making in most cases[1], [2]. Before discussing the main topic of this paper,we first describe the characteristics and categories ofspatial and temporal data, the three main features ofwhich are 1) data scale, 2) data type and 3) process type.
Large-scale data is commonly used in spatio-temporalsystems. This means that in such a large structure,continuous and automatic analysis is imperative. Forexample, in a climate change system, usually 10 years’worth or more of values for variables such astemperature, pressure, rainfall, humidity, amongst manyothers, are used for forecasting.
This information needs to be stored in such a way thatthe accuracy and integrity of the information are notdamaged in storage and retrieval operations. In addition,because the monitoring process is more important intemporal systems, logs and stream files are alwaysupdated and this leads to an even greater volume of data[1]. In addition, the data types in spatio-temporal systemsare very diverse. The data type is considered the secondmost important feature in such systems because lines,graphical data and images require some specialconditions to be considered in save and retrieveoperations.
Moreover, it is needed to have some data sequence inthese systems according to the time. In other words, timeas a fourth dimension of information has a principal roleto play in spatio-temporal systems.
For instance, if we want to record rainfall in adeterministic period and area, we need to have data aboutthe location, i.e. spatial data and some information aboutthe time and sequence, i.e., temporal data, becauseknowledge on rainfall depends on both place and time.
As another example for variant of data, we canmention to Satellite Images Time Series (SITS) with highresolution in spatial or temporal values. In recent years,this kind of data has been used extensively ingeographical information systems (GIS) [3].
The third feature that needs to be considered in aspatio-temporal system is the process or analysis type.
It is essential to run some queries with spatial and/ortemporal specifications in addition to common forms ofqueries which are defined in relevant data structures.
Such query operations are performed by threedifferent types of function: spatial functions, temporalfunctions and spatio-temporal functions [4], [5]. There isa difference between the types of queries these functionsperform; a traditional database may contain data onrainfall amounts in specific locations and we cancalculate the sum or maximum of that variable.
However, we want to do more than that with currentand future systems; we may want, for example, togenerate a query to find the nearest rain-rich places in ariver or places where the maximum temperature hasdecreased in the last five years.
A. Madraky, Z. A. Othman, A. R. Hamdan
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Generally, there needs to be an analytic infrastructurefor analysing such phenomena that have both spatial andtemporal properties. However, obviously, the analyticoperations in spatio-temporal systems are complex.
Hence the development of suitable methods ofanalysis is a major challenge in this field [6].
To address this challenge, in this paper, we investigatethe main analytic functions by categorizing them into twogroups in terms of (1) process type and (2) algorithmused. The first group consists of three types of processingfunction, namely, spatial, temporal and spatio-temporalprocessing, as mentioned above. In the case of spatialanalytic functions, the main analytic elements areassociated with information on a particular place.
This kind of processing is of great interest to bothusers and experts because there is increasing access toand use of new technologies for collecting placeinformation such as global positioning systems (GPS),remote sensing with high resolution and geographicalinternet-based services and monitoring systems. As aconsequence of the need for geographic knowledgediscovery, spatial data mining facilities have emergedtogether with some theories and methods to try and fullyunderstand and utilize the mass of complicated data thatis being collected by these facilities [7].
In the case of temporal analytic functions, data isinvestigated based on event sequences. This analysis iscalled temporal data mining. Three time-related levelshave been defined and each has specific analyticfunctions. In the first level, there are no time-relatedvariables and the data arrangement indicates thesequence of the data [8]. The second level, which isnormally used for online information, is called the datastream. Data streams have one explicit variable at leastfor recording the time of events. This kind of analysis isusually performed after data monitoring [9].
In the third level, time stamps are applied to log thetime values and analyse them. This level is similar to thesecond level, but it is possible to define time intervals[9], [10], [11], [12].
In the case of spatio-temporal analytic functions, placeand time values have a simultaneous role to play in ananalysis. These functions normally are more complicatedand more useful than the above described functions. Inspatio-temporal data analysis, there is a substantialamount of previous position-related events. This aspect iseven more significant when geometric algorithms areused for processing and investigating geographic datachanges [13]. Analytic functions can also be categorizedaccording to the algorithms used. Each function utilizesone or more data mining algorithms that are selected forspecific requirements and operational environments.
There are three main types of algorithm that performspecific tasks: classification, clustering and association ofrules. A comprehensive review of Formal ConceptAnalysis (FCA) methods for the period from 2003 to2011 can be found in [14]. In classification, similarobjects are assigned to a particular class and so it isessential to define a model for the initial data.
The classification task is performed primarily insupervised learning methods. The neural network, K-nearest neighbour and rough set approaches are morecommonly used in spatio-temporal systems. An artificialneural network categorizes initial data based on aweighted graph and learning concepts are implementedby weights.
Some of the neural network-oriented methodsemployed in spatio-temporal systems are presented in[15]-[18]. The K-nearest neighbour method classifiesnumeric data after determining their classes. The classdepends on the object distances in a particular zone(K);the class of an object is specified by the minimumdistance. This algorithm is used for spatio-temporalanalysis in [19]-[21].
The rough set is another classification technique and isused to structure relationships in ambiguous and noisydata. All of the variables in this method are discrete. Arough set method finds equivalence classes based ontraining data.
Each class has some attributes that are indiscerniblebecause these classes cannot be determined in terms ofavailable data. Therefore, an approximate class is definedto identify the data; to do so, the method uses an upperand lower approximation set of a particular class [12],[22], [23]. Clustering or segmentation is another task indata mining. Data groups are identified by similarity intheir attribute values. Each cluster includes some similarobjects.
One of the most relevant clustering methods forspatio-temporal data is the density-based or density-oriented method [24]-[26]. Other algorithms used inclustering have been presented in [27]-[29].
In the case of the association of rules task, data iscategorized based on pattern repetition. Generally, thistask is performed in two stages: (1) identifying repeatedobjects and (2) analysing them to extract rules. The ruleshave two measures called Confidence and Support.
Confidence is defined as the ratio of records that havea rule to the records that have conditions. This measuredetermines rule accuracy. Support is defined as the ratioof records that have a rule to whole records. Thismeasure determines rule usefulness. Some otheralgorithms based on associated rules for spatio-temporaldata are presented in [30]-[32].
In the above introduction, we briefly categorized theanalytic function sand their basic specifications. Theremainder of this paper is organized as follows: in thesecond section, we survey the recent literature on appliedor extended methods and also explain the difficultiesencountered in the knowledge representation of spatialand temporal systems.
In the third section, we discuss our hair-oriented datamodel (HODM), which is a nature-inspired model forspatio-temporal data, and describe and implement threekey analytic functions of this model that can improvedata representation and response time to queries. In thelast section, we summarize the advantages of ourproposed model and discuss directions for future work.
A. Madraky, Z. A. Othman, A. R. Hamdan
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II. Representation of Analytic Functionin Spatio-Temporal Models
In this section, we review some new, analyticalmethods that have been published from 2006 to 2013 inhigh-impact journals. It is important to identify thealgorithms that are currently the most commonly used inspatial and temporal data processing in order to definethe functions that are required for knowledgerepresentation.
The methods proposed in the identified articles areused for mining processes to meet knowledgerequirements. In Table I, we categorize the journalarticles according to the type of algorithm used in theproposed method and describe in brief the contributionswith respect to analytic functions that are addressed byour method elaborated in section III.
It should be noted that we focus on a limited numberof works as we consider this makes the comparison ofthese methods with HODM more valid. It should also benoted that some articles appear more than once in thetable because they use or combine more than onealgorithm.
Our understanding of a topic improves if we canaccess an accurate representation of the analytical resultsof our enquiries [36]. However, users and experts wouldnot only like to gain a proper understanding of a topicfrom the knowledge hidden in a data set by using themost reliable method, they would also like to access thisknowledge by the fastest means. Hidden knowledgeincludes static parts such as facts or concepts anddynamic parts such as abilities, skills, procedures andactions.
Three performance-based measures have been definedto assess the ‘understandability performance’ ofindividual participants when using a spatio-temporalsystem in data analysis: (1) Understandability Time(UT), (2) Understanding Effectiveness (UEffec) and (3)Understandability Efficiency (UEffic). The first, UT,relates to the time of understanding while UEffec is thenumber of correct answers, which reflects how well theparticipants perform the required understandability tasks,and UEffic is the number of correct answers divided byUT, which determines the performance of the participantsin regards of their effort in terms of time spent [37].
TABLE ISPATIO-TEMPORAL ANALYTICAL METHODS IN ARTICLES PUBLISHED FROM 2006 TO 2013
Main ContributionsPaper
NumberAlgorithm
Identifies scalable issues in the processing of Continuous K-Nearest Neighbour QueriesProposes an index structure, namely, the CI-treeDemonstrates the effectiveness and the efficiency of the CI-tree
[19]Classification(Nearest Neighbour)
Proposes a feature extraction method called Adaptive Locality Preserving Projection (ALPP)Proposes a temporal information extraction method called Non-base Central-Difference Action Vector(NCDAV)
[20]
Proposes a framework to extract discriminant features for action recognition[21]Simulates Hopfield networks through on-demand computation for large-scale static optimization problems[33]Classification
(Neural Networks) Introduces two spatio-temporal biologically inspired methods for the pre-processing and learning of speechsignals
[15]
Introduces a new type of adaptive wavelet network, Develops a hybrid learning scheme[16]Proposes a new dynamic evolving spiking neural networks model called deSNN for faster online learning[17]Proposes two new approaches of spatio-temporal data classification using complex-valued neural networks[18]Proposes a new methodology so as to overcome the learning problem in thermal engineering by multilayerperceptron
[34]
Applies rough set definitions for topological relationships[22]Classification(Rough Set) Proposes a change and connection mining algorithm for discovering a time delay by using the rough set
approach[12]
Proposes a new density-based clustering algorithm, the ST-DBSCAN, which is related with the identificationof (i) core objects, (ii) noise objects, and (iii) adjacent clusters
[27]Clustering
Develops a rich database on choreographic information and uses it as an approach to analyse temporal data[6]Satisfaction of the information need of ‘situational awareness’ for event control[24]Introduces a novel approach for selective Spatio-Temporal Interest Points STIP detectionIntroduces a novel vocabulary building strategy by combining a pyramid structure and vocabularycompressionEvaluates the approach on popular benchmark datasets
[28]
Proposes a new hybrid approach to spatio-temporal seismic clustering[26]Determines the inference of latent features associated with legislators through the analysis of spatio-temporalstructure
[29]
Improves the visualization of electrically active cells by performing spatio-temporal clustering beforeapplying kernel density estimation
[25]
Compares the performance of approaches in clustering subscriptions[35]Proposes a new algorithm, ARMADA, to discover frequent temporal patterns and to generate richer interval-based temporal association rulesIntroduces a maximum gap time constraint that can be used to get rid of insignificant patterns and rules
[30]Association Rules
Proposes a data mining system to deal with very large spatio-temporal data sets[1]Proposes a new approach to mine context-based positive and negative spatial association rules[31]Introduces a methodology for spatial–temporal association rules and multi-level directed graphs withdifferent levels of space and time granularity
[32]
A. Madraky, Z. A. Othman, A. R. Hamdan
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Since the data and the requirements are highlycomplex in spatio-temporal systems, understandability ofthe knowledge representation models for these systems ismore important than for simpler systems [38].
There are quite a few difficulties in representingknowledge and the information obtained from spatio-temporal data. Here, we concentrate on the issues ofquerying similarity and assigning membership. One ofthe main and most relevant features of hidden knowledgein spatio-temporal data is region similarities [39], whichcan, for instance, allow us to identify similar areas basedon rainfall or drought patterns.
Some of the main methods for similarity calculationare mentioned in Table I. However, the lack of structuralattributes for storing results means we have to repeat theprocess if we want to have accurate similarityinformation. We therefore need to be able to save resultsin specific attributes to avoiding reworking. This leads tobetter analysis based on extendable spatial and temporalqueries [40]. The second feature to consider in spatio-temporal knowledge representation is showing a positionmembership in a class or cluster [41]. The membershipfunction can work based on some attributes of a position.
It maybe performed in terms of an area or positiondistances. The membership function can also use fuzzyconcepts and identify memberships by probabilisticvalues [42]. As shown in Table I, classification,clustering and association rules are utilized to definefunctions, but there is no structural place for knowledgerepresentation. The general solution to the aboveproblems involves the use of additive analytic attributes.
These structural attributes are updated with thechanging of data values and they are always kept up todate [43]. While this approach uses more storage it doesreduce repeated processes, which allows access to datamining results in a shorter response time and with greaterreliability. In the next section, we define and implement adata model that adopts the additive analytic attributesapproach. This data model needs to have structuralattributes and analytic functions to solve similarityqueries and membership function problems. It also has tohave spatial and temporal features to define shapes andprocess them.
III. Hair-Oriented Data Model
Generally, spatio-temporal data has two mainproperties that distinguish it from other data types. Thefirst one is that data is allocated to a specific location.
The second property is that new attribute values areproduced and stored for a specific point in terms of thetime without removing the previous values.
In these respects, we find that spatio-temporalproperties and behaviour have similarities with those ofthe structure of natural hair because each strand of hair isallocated to one particular position and it grows byproducing new cells without removing previous cells.
Moreover, the hair structure and its growth can bedefined in a similar way to spatio-temporal data.
That is to say, some hair attributes such as Strength(Importance), Direction (Orientation), Proximity(Neighbourhood) and some operations such as Growing(Insertion), Cutting (Deletion), Washing (Cleaning) havemuch in common with the concepts employed in datadefinition. The HODMand the specifications of itsattributes and functions has previously been introducedin [44]. For ease of reference, Fig. 1 illustrates thespecifications of the HODM and the correspondingfeatures in hair’s natural structure.
Fig. 1. Comparison of hair’s natural structureand hair-oriented data model
In terms of the conditions in the spatio-temporal datamodel and due to the structure and behaviour of naturalhair, the functions in the HODM are categorized intothree types. The first consists of maintenance functionsfor Creating, Erasing, Insertion, Updating and Deletingdata.
The corresponding functions in natural hair areImplanting, Falling, Growing, Washing and Cutting. Thesecond type functions that need to be applied to spatio-temporal data consists of security functions that protectdata via regularity reduction, the authenticationprocedure and unreal data, which corresponds toTangling, Covering and Wig in relation to natural hair.
The third type consists of analytic functions, whichequate to Combing, Plaiting and Colouring in relation tonatural hair. This third type of function is the focus ofthis paper. However, first we explain the model structureand how it can be applied to a sample data set. We usethe El Nino data set saved in the UCI Repository. Thedata set contains oceanographic and surfacemeteorological readings taken from a series of buoyspositioned throughout the equatorial Pacific. The data isexpected to aid in the understanding and prediction of ElNino/Southern Oscillation (ENSO) cycles [45].
The main file of El Nino is tao-all2.dat with 178 080rows containing the data for 7 March 1980 to 3 May1998.The data set is categorized as spatio-temporal data.
We used five sub files from the main El Nino fileconsisting of 782 rows containing the data for 23 May1998 to 5 June 1998. We considered this file as aprototype.
The El Nino data relates to climate change in relevantpositions. We selected the data values for two particularpositions (Buoy1 and Buoy2), which are sorted bylocation number. The values of those positions areillustrated in Table II.
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number, day, latitude, longitude, zonal(west<0, east>0), meridionalnorth>0), relative humidity, air temperature and seasurfacebuoys foadvantage ofredundancy indata so th
theare repeated in all of the rows
Fig.in the Name, Positionenables usdata transfer costs in large databases. In other wordspatial data
Copyright © 2014 Praise Worthy Prize S.r.l.
Buoy
The data consistnumber, day, latitude, longitude, zonal(west<0, east>0), meridionalnorth>0), relative humidity, air temperature and seasurfacebuoys foadvantage ofredundancy indata so th
The location numberthe coordinates inare repeated in all of the rows
However,Fig. 2in the Name, Positionenables usdata transfer costs in large databases. In other wordspatial data
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Buoy11111111111112222222222222
The data consistnumber, day, latitude, longitude, zonal(west<0, east>0), meridionalnorth>0), relative humidity, air temperature and seasurface (s.s.)buoys foadvantage ofredundancy indata so th
The location numbercoordinates in
are repeated in all of the rowsHowever,
, thein the Name, Positionenables usdata transfer costs in large databases. In other wordspatial data
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Buoy
The data consistnumber, day, latitude, longitude, zonal(west<0, east>0), meridionalnorth>0), relative humidity, air temperature and sea
(s.s.)r some locations.
advantage ofredundancy indata so that integrity and consistency
The location numbercoordinates in
are repeated in all of the rowsHowever,
the location specificationsin the Name, Positionenables us to decrease database size, response time anddata transfer costs in large databases. In other wordspatial data (the
Copyright © 2014 Praise Worthy Prize S.r.l.
Day123456789
10111213123456789
10111213
The data consistnumber, day, latitude, longitude, zonal(west<0, east>0), meridionalnorth>0), relative humidity, air temperature and sea
(s.s.) temperature. Datar some locations.
advantage of theredundancy in a
integrity and consistencyThe location number
coordinates inare repeated in all of the rows
in HODM structurelocation specifications
in the Name, Positionto decrease database size, response time and
data transfer costs in large databases. In other word(the
Copyright © 2014 Praise Worthy Prize S.r.l.
Day1234567891011121312345678910111213
The data consistnumber, day, latitude, longitude, zonal(west<0, east>0), meridionalnorth>0), relative humidity, air temperature and sea
temperature. Datar some locations.
the HODM isa data
integrity and consistencyThe location number
coordinates inare repeated in all of the rows
in HODM structurelocation specifications
in the Name, Positionto decrease database size, response time and
data transfer costs in large databases. In other wordstatic part
Copyright © 2014 Praise Worthy Prize S.r.l.
The data consistsnumber, day, latitude, longitude, zonal(west<0, east>0), meridionalnorth>0), relative humidity, air temperature and sea
temperature. Datar some locations.
HODM isdata
integrity and consistencyThe location number
theare repeated in all of the rows
in HODM structurelocation specifications
in the Name, Positionto decrease database size, response time and
data transfer costs in large databases. In other wordstatic part
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Latitude8.968.958.968.968.968.968.978.968.978.978.968.968.964.934.924.924.924.934.934.934.924.934.934.934.934.93
of the following variables: buoynumber, day, latitude, longitude, zonal(west<0, east>0), meridionalnorth>0), relative humidity, air temperature and sea
temperature. Datar some locations.
HODM isset
integrity and consistencyThe location number is
Latitude and Longitude columnsare repeated in all of the rows
in HODM structurelocation specifications
X and Positionto decrease database size, response time and
data transfer costs in large databases. In other wordstatic part
Copyright © 2014 Praise Worthy Prize S.r.l.
Latitude8.968.958.968.968.968.968.978.968.978.978.968.968.964.934.924.924.924.934.934.934.924.934.934.934.934.93
of the following variables: buoynumber, day, latitude, longitude, zonal(west<0, east>0), meridionalnorth>0), relative humidity, air temperature and sea
temperature. Datar some locations. As mentioned in
HODM isset by removing repeated spatial
integrity and consistencyis saved in
Latitude and Longitude columnsare repeated in all of the rows
in HODM structurelocation specifications
X and Positionto decrease database size, response time and
data transfer costs in large databases. In other wordstatic part of the data)
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Latitude
of the following variables: buoynumber, day, latitude, longitude, zonal(west<0, east>0), meridional (mer.)north>0), relative humidity, air temperature and sea
temperature. DataAs mentioned in
HODM is that it is ableby removing repeated spatial
integrity and consistencysaved in
Latitude and Longitude columnsare repeated in all of the rows for
in HODM structurelocation specifications
X and Positionto decrease database size, response time and
data transfer costs in large databases. In other wordof the data)
Copyright © 2014 Praise Worthy Prize S.r.l.
of the following variables: buoynumber, day, latitude, longitude, zonal
(mer.)north>0), relative humidity, air temperature and sea
was collectedAs mentioned in
that it is ableby removing repeated spatial
integrity and consistencysaved in the
Latitude and Longitude columnsfor each position
in HODM structure, whichlocation specifications are saved
X and Positionto decrease database size, response time and
data transfer costs in large databases. In other wordof the data)
A. Madraky, Z. A. Othman, A. R. Hamdan
- All rights reserved
Longitude-140.32-140.32-140.32-140.34-140.33-140.33-140.32-140.33-140.33-140.32-140.32-140.33-140.33-139.87-139.86-139.87-139.88-139.87-139.87-139.86-139.87-139.87-139.86-139.85-139.86-139.87
of the following variables: buoynumber, day, latitude, longitude, zonal
(mer.)north>0), relative humidity, air temperature and sea
was collectedAs mentioned in
that it is ableby removing repeated spatial
integrity and consistency isthe
Latitude and Longitude columnseach position, whichare saved
X and Positionto decrease database size, response time and
data transfer costs in large databases. In other wordof the data) is
Fig.
A. Madraky, Z. A. Othman, A. R. Hamdan
All rights reserved
Longitude140.32140.32140.32140.34140.33140.33140.32140.33140.33140.32140.32140.33140.33139.87139.86139.87139.88139.87139.87139.86139.87139.87139.86139.85139.86139.87
of the following variables: buoynumber, day, latitude, longitude, zonal
winds (south<0,north>0), relative humidity, air temperature and sea
was collectedAs mentioned in
that it is ableby removing repeated spatial
increased.Buoy column and
Latitude and Longitude columnseach position, which is illustrated inare saved
Y attributes.to decrease database size, response time and
data transfer costs in large databases. In other wordis saved in the
Fig. 2.
A. Madraky, Z. A. Othman, A. R. Hamdan
All rights reserved
EL
Longitude140.32140.32140.32140.34140.33140.33140.32140.33140.33140.32140.32140.33140.33139.87139.86139.87139.88139.87139.87139.86139.87139.87139.86139.85139.86139.87
of the following variables: buoy(zon.)
winds (south<0,north>0), relative humidity, air temperature and sea
was collectedAs mentioned in
that it is ableby removing repeated spatial
increased.Buoy column and
Latitude and Longitude columnseach position
is illustrated inare saved just one time
attributes.to decrease database size, response time and
data transfer costs in large databases. In other wordsaved in the
2. El Nino Sub
A. Madraky, Z. A. Othman, A. R. Hamdan
All rights reserved
L NINO
of the following variables: buoy(zon.)
winds (south<0,north>0), relative humidity, air temperature and sea
was collected from theAs mentioned in [4
that it is able to reduceby removing repeated spatial
increased.Buoy column and
Latitude and Longitude columnseach position.
is illustrated injust one time
attributes.to decrease database size, response time and
data transfer costs in large databases. In other wordsaved in the
El Nino Sub
A. Madraky, Z. A. Othman, A. R. Hamdan
All rights reserved
INO SZon.Winds
of the following variables: buoy(zon.) winds
winds (south<0,north>0), relative humidity, air temperature and sea
from the44], one
to reduceby removing repeated spatial
increased.Buoy column and
Latitude and Longitude columns
is illustrated injust one time
attributes.to decrease database size, response time and
data transfer costs in large databases. In other wordssaved in the
El Nino Sub
A. Madraky, Z. A. Othman, A. R. Hamdan
TABLEUBD
Zon.Winds-6.3-5.7-6.2-6.4-4.9-6.3-6.7-6.3-6.3-4.2-6.8-7.1-6.7-2.90.4-4.0-6.3-4.1-5.4-4.7-2.5-5.1-1.3-5.4-6.2-4.8
of the following variables: buoywinds
winds (south<0,north>0), relative humidity, air temperature and sea
from the, one
to reduceby removing repeated spatial
Buoy column andLatitude and Longitude columns
is illustrated injust one time
Thisto decrease database size, response time and
s, thesaved in the root
El Nino Subdata in HODM for Position Buoy 1
A. Madraky, Z. A. Othman, A. R. Hamdan
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TABLEDATA
Zon.Winds
of the following variables: buoywinds
winds (south<0,north>0), relative humidity, air temperature and sea
from the, one
to reduceby removing repeated spatial
Buoy column andLatitude and Longitude columns
is illustrated injust one time
Thisto decrease database size, response time and
theoot
ata in HODM for Position Buoy 1
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TABLE IIATA IN O
ofdata) is
unmovable positions thatspecifications ofcoordinate,savedchangeby adding data values.
HODM, eachfields in tablevalues and additive attributes includDphasethis structure.
2. Proximity and Type are assigned
ata in HODM for Position Buoy 1
A. Madraky, Z. A. Othman, A. R. Hamdan
IIORIGINAL
Mer.Winds
of the hdata) is
To elaborate, theunmovable positions thatspecifications ofcoordinate,savedchangeby adding data values.
ThisHODM, eachfields in tablevalues and additive attributes includDirection,phasethis structure.
The i2. Proximity and Type are assigned
ata in HODM for Position Buoy 1
A. Madraky, Z. A. Othman, A. R. Hamdan
RIGINAL
Mer.Winds-6.4-3.6-5.8-5.3-6.2-4.9-3.7-4.8-4.9-2.5-2.4-3.2-4.7-1.2-1.6-4.6-3.23.31.90.9-0.92.00.7-1.2-4.02.6
the hdata) is
To elaborate, theunmovable positions thatspecifications ofcoordinate,saved without redundancychangeby adding data values.
ThisHODM, eachfields in tablevalues and additive attributes includ
irection,phase usingthis structure.
The i2. Proximity and Type are assigned
ata in HODM for Position Buoy 1
A. Madraky, Z. A. Othman, A. R. Hamdan
International Review on Computers and Software, Vol. 9, N. 3
RIGINAL FORM
Mer.Winds6.43.65.85.36.24.93.74.84.92.52.43.24.71.21.64.63.23.31.90.90.92.00.71.24.02.6
the hair anddata) is saved in the
To elaborate, theunmovable positions thatspecifications ofcoordinate,
without redundancy, is
by adding data values.This is one of the
HODM, eachfields in tablevalues and additive attributes includ
irection,using
this structure.The initial state of
2. Proximity and Type are assigned
ata in HODM for Position Buoy 1
A. Madraky, Z. A. Othman, A. R. Hamdan
International Review on Computers and Software, Vol. 9, N. 3
ORM
Mer.Winds
air andsaved in the
To elaborate, theunmovable positions thatspecifications ofcoordinate, neighbour
without redundancysaved in the body part
by adding data values.is one of the
HODM, eachfields in tablevalues and additive attributes includ
Proximity andusing Oracle, we
this structure.nitial state of
2. Proximity and Type are assigned
ata in HODM for Position Buoy 1
A. Madraky, Z. A. Othman, A. R. Hamdan
International Review on Computers and Software, Vol. 9, N. 3
Humidity
air and thesaved in the
To elaborate, theunmovable positions thatspecifications of
neighbourwithout redundancy
saved in the body partby adding data values.
is one of thehair is defined by a record that has some
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roximity andOracle, we
nitial state of2. Proximity and Type are assigned
ata in HODM for Position Buoy 1
A. Madraky, Z. A. Othman, A. R. Hamdan
International Review on Computers and Software, Vol. 9, N. 3
Humidity83.586.483
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the temporal datasaved in the b
To elaborate, theunmovable positions thatspecifications of h
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saved in the body partby adding data values.
is one of theair is defined by a record that has some
formvalues and additive attributes includ
roximity andOracle, we
nitial state of2. Proximity and Type are assigned
ata in HODM for Position Buoy 1
International Review on Computers and Software, Vol. 9, N. 3
Humidity83.586.483
82.287.391.594.192
86.987.386
82.281.387.682.383.087.684.681.980.384.284.680.083.482.782.3
temporal databody of
To elaborate, theunmovable positions that
hair. Spatial data such as name,neighbourhood and position relationships are
without redundancysaved in the body part
by adding data values.key
air is defined by a record that has someand t
values and additive attributes includroximity andOracle, we
nitial state of the2. Proximity and Type are assigned
International Review on Computers and Software, Vol. 9, N. 3
Humidity
temporal dataody of
HODM is usedunmovable positions that
air. Spatial data such as name,hood and position relationships are
without redundancy,saved in the body part
key advantages of this modelair is defined by a record that has some
and this record isvalues and additive attributes includ
roximity andemployed a
the analytic val2. Proximity and Type are assigned
International Review on Computers and Software, Vol. 9, N. 3
Air Temp.
temporal dataody of the h
HODM is usedare
air. Spatial data such as name,hood and position relationships are
but temporal datasaved in the body part
advantages of this modelair is defined by a record that has some
his record isvalues and additive attributes includ
Type. In the implementationemployed a
analytic val2. Proximity and Type are assigned
International Review on Computers and Software, Vol. 9, N. 3
Air Temp.27.3226.727.3627.3227.0926.8226.6226.8927.4426.6227.627.8727.7526.8427.3028.0027.6127.6927.9628.0827.6127.4227.6527.8927.7727.42
temporal data (thethe hair.
HODM is usedadapted to
air. Spatial data such as name,hood and position relationships are
but temporal datasaved in the body part of the hair
advantages of this modelair is defined by a record that has some
his record isvalues and additive attributes includ
ype. In the implementationemployed a
analytic val2. Proximity and Type are assigned
International Review on Computers and Software, Vol. 9, N. 3
Air Temp.27.3226.727.3627.3227.0926.8226.6226.8927.4426.6227.627.8727.7526.8427.3028.0027.6127.6927.9628.0827.6127.4227.6527.8927.7727.42
(theair.
HODM is usedadapted to
air. Spatial data such as name,hood and position relationships are
but temporal dataof the hair
advantages of this modelair is defined by a record that has some
his record isvalues and additive attributes includ
ype. In the implementationemployed a nested table
analytic val2. Proximity and Type are assigned the value of n
International Review on Computers and Software, Vol. 9, N. 3
Air Temp.
dynamic part
HODM is usedadapted to
air. Spatial data such as name,hood and position relationships are
but temporal dataof the hair
advantages of this modelair is defined by a record that has some
his record is avalues and additive attributes includ
ype. In the implementationnested table
analytic values is shown in Fig.the value of n
International Review on Computers and Software, Vol. 9, N. 3
S.S.Temp.
dynamic part
HODM is usedadapted to the
air. Spatial data such as name,hood and position relationships are
but temporal dataof the hair
advantages of this modelair is defined by a record that has some
a mixvalues and additive attributes including
ype. In the implementationnested table
ues is shown in Fig.the value of n
International Review on Computers and Software, Vol. 9, N. 3
S.S.Temp.27.5727.6227.6827.7
27.8527.9828.0427.9828.1328.1428.0928.1528.1928.3528.4828.6128.5528.3528.4828.5428.7228.6628.7328.8428.6328.48
dynamic part
to representthe
air. Spatial data such as name,hood and position relationships are
but temporal data, which canof the hair and
advantages of this modelair is defined by a record that has some
mixtureing
ype. In the implementationnested table
ues is shown in Fig.the value of n
International Review on Computers and Software, Vol. 9, N. 3
S.S.Temp.27.5727.6227.6827.7
27.8527.9828.0427.9828.1328.1428.0928.1528.1928.3528.4828.6128.5528.3528.4828.5428.7228.6628.7328.8428.6328.48
dynamic part
to representstructural
air. Spatial data such as name,hood and position relationships are
which canand is grown
advantages of this modelair is defined by a record that has some
ture of dataStrength,
ype. In the implementationnested table to define
ues is shown in Fig.the value of null.
International Review on Computers and Software, Vol. 9, N. 3
S.S.Temp.
dynamic part of the
to representstructural
air. Spatial data such as name,hood and position relationships are
which canis grown
; in theair is defined by a record that has some
of datatrength,
ype. In the implementationto define
ues is shown in Fig.ull.
International Review on Computers and Software, Vol. 9, N. 3
of the
to representstructural
air. Spatial data such as name,hood and position relationships are
which canis grown
n theair is defined by a record that has some
of datatrength,
ype. In the implementationto define
ues is shown in Fig.
International Review on Computers and Software, Vol. 9, N. 3
of the
to representstructural
air. Spatial data such as name,hood and position relationships are
which canis grown
n theair is defined by a record that has some
of datatrength,
ype. In the implementationto define
ues is shown in Fig.
Copyright © 2014 Praise Worthy Prize S.r.l.
Strength value is consideredimportanceinserted
based on the structure through the save spatial data in arow and temporal data in a nested tablefinal schema is shown in Fig.
defined in two rows ofdisplay
III.1.
theandclassification or clustering methodsdefined according to
data mining algorithmsalgorithms. In theperformstructural attributes by embedding analysis functions.
definition and decrease reprocess analytical findings by changing or recordingtheseProximity and Type.
position is determined byattribute can be declared in numeric form as a weight orintype. Ituncertain data
significant positions are identified in order tothem. The other analytic attributesProximity and Typeby theare
Copyright © 2014 Praise Worthy Prize S.r.l.
TheStrength value is consideredimportanceinserted
The second pbased on the structure through the save spatial data in arow and temporal data in a nested tablefinal schema is shown in Fig.
It should bedefined in two rows ofdisplay
III.1.
As mentioned above, tthe three analytical functionsand Colourclassification or clustering methodsdefined according to
These functions do notdata mining algorithmsalgorithms. In theperformstructural attributes by embedding analysis functions.
The new attributesdefinition and decrease reprocess analytical findings by changing or recordingtheseProximity and Type.
The importance of the values that are allocated to aposition is determined byattribute can be declared in numeric form as a weight or
string form as a fuzzy value basetype. Ituncertain data
In adsignificant positions are identified in order tothem. The other analytic attributesProximity and Typeby there explained in following
Copyright © 2014 Praise Worthy Prize S.r.l.
The initial value ofStrength value is consideredimportanceinserted the
The second pbased on the structure through the save spatial data in arow and temporal data in a nested tablefinal schema is shown in Fig.
should bedefined in two rows ofdisplays particular position specifications.
Definitions of the
As mentioned above, tthree analytical functionsColour
classification or clustering methodsdefined according to
These functions do notdata mining algorithmsalgorithms. In theperform data modelling and representation based on newstructural attributes by embedding analysis functions.
The new attributesdefinition and decrease reprocess analytical findings by changing or recording
structuralProximity and Type.
The importance of the values that are allocated to aposition is determined byattribute can be declared in numeric form as a weight or
string form as a fuzzy value basetype. It isuncertain data
In addition,significant positions are identified in order tothem. The other analytic attributesProximity and Typeby the Combing, Plaiting and Colouring
explained in following
Copyright © 2014 Praise Worthy Prize S.r.l.
initial value ofStrength value is consideredimportance for instance
the data values of two positions as an instance.The second p
based on the structure through the save spatial data in arow and temporal data in a nested tablefinal schema is shown in Fig.
should bedefined in two rows of
particular position specifications.
Definitions of the
As mentioned above, tthree analytical functionsColouring. These functions are used
classification or clustering methodsdefined according to
These functions do notdata mining algorithmsalgorithms. In the
data modelling and representation based on newstructural attributes by embedding analysis functions.
The new attributesdefinition and decrease reprocess analytical findings by changing or recording
structuralProximity and Type.
The importance of the values that are allocated to aposition is determined byattribute can be declared in numeric form as a weight or
string form as a fuzzy value baseis used for reporting, quer
uncertain datadition,
significant positions are identified in order tothem. The other analytic attributesProximity and Type
Combing, Plaiting and Colouringexplained in following
Copyright © 2014 Praise Worthy Prize S.r.l.
initial value ofStrength value is considered
for instancedata values of two positions as an instance.
The second pbased on the structure through the save spatial data in arow and temporal data in a nested tablefinal schema is shown in Fig.
should bedefined in two rows of
particular position specifications.
Definitions of the
As mentioned above, tthree analytical functions
ing. These functions are usedclassification or clustering methodsdefined according to
These functions do notdata mining algorithmsalgorithms. In the
data modelling and representation based on newstructural attributes by embedding analysis functions.
The new attributesdefinition and decrease reprocess analytical findings by changing or recording
structuralProximity and Type.
The importance of the values that are allocated to aposition is determined byattribute can be declared in numeric form as a weight or
string form as a fuzzy value baseused for reporting, quer
uncertain data accordingdition, by using this
significant positions are identified in order tothem. The other analytic attributesProximity and Type
Combing, Plaiting and Colouringexplained in following
Copyright © 2014 Praise Worthy Prize S.r.l.
initial value ofStrength value is considered
for instancedata values of two positions as an instance.
The second positionbased on the structure through the save spatial data in arow and temporal data in a nested tablefinal schema is shown in Fig.
emphasized thatdefined in two rows of
particular position specifications.
Definitions of the
As mentioned above, tthree analytical functions
ing. These functions are usedclassification or clustering methodsdefined according to
These functions do notdata mining algorithmsalgorithms. In the
data modelling and representation based on newstructural attributes by embedding analysis functions.
The new attributesdefinition and decrease reprocess analytical findings by changing or recording
attributesProximity and Type.
The importance of the values that are allocated to aposition is determined byattribute can be declared in numeric form as a weight or
string form as a fuzzy value baseused for reporting, quer
accordingby using this
significant positions are identified in order tothem. The other analytic attributesProximity and Type
Combing, Plaiting and Colouringexplained in following
Copyright © 2014 Praise Worthy Prize S.r.l.
initial value ofStrength value is considered
for instancedata values of two positions as an instance.
ositionbased on the structure through the save spatial data in arow and temporal data in a nested tablefinal schema is shown in Fig.
emphasized thatdefined in two rows of
particular position specifications.
Definitions of the
As mentioned above, tthree analytical functions
ing. These functions are usedclassification or clustering methodsdefined according to the
These functions do notdata mining algorithms
HODMdata modelling and representation based on new
structural attributes by embedding analysis functions.The new attributes
definition and decrease reprocess analytical findings by changing or recording
attributesProximity and Type.
The importance of the values that are allocated to aposition is determined byattribute can be declared in numeric form as a weight or
string form as a fuzzy value baseused for reporting, quer
accordingby using this
significant positions are identified in order tothem. The other analytic attributesProximity and Type, which in the HODM are
Combing, Plaiting and Colouringexplained in following
Copyright © 2014 Praise Worthy Prize S.r.l.
Direction is equal to Position andStrength value is considered
for instance. As can be seendata values of two positions as an instance.
osition (Buoy2)based on the structure through the save spatial data in arow and temporal data in a nested tablefinal schema is shown in Fig.
emphasized thatdefined in two rows of
particular position specifications.
Definitions of the Proposed
As mentioned above, tthree analytical functions
ing. These functions are usedclassification or clustering methods
the natural specification of hair.These functions do not
data mining algorithmsHODM
data modelling and representation based on newstructural attributes by embedding analysis functions.
The new attributes allow us todefinition and decrease reprocess analytical findings by changing or recording
attributes, which are
The importance of the values that are allocated to aposition is determined byattribute can be declared in numeric form as a weight or
string form as a fuzzy value baseused for reporting, quer
according to data reliability or probability.by using this
significant positions are identified in order tothem. The other analytic attributes
, which in the HODM areCombing, Plaiting and Colouring
explained in following
Copyright © 2014 Praise Worthy Prize S.r.l.
Direction is equal to Position andStrength value is considered
As can be seendata values of two positions as an instance.
(Buoy2)based on the structure through the save spatial data in arow and temporal data in a nested tablefinal schema is shown in Fig.
emphasized thatthe hair
particular position specifications.
Proposed
As mentioned above, this paper focuses on definingthree analytical functions
ing. These functions are usedclassification or clustering methods
natural specification of hair.involve the proposition of
data mining algorithms, butHODM, we propose a new way
data modelling and representation based on newstructural attributes by embedding analysis functions.
allow us todefinition and decrease response time. It is possible toprocess analytical findings by changing or recording
, which are
The importance of the values that are allocated to aposition is determined by theattribute can be declared in numeric form as a weight or
string form as a fuzzy value baseused for reporting, quer
to data reliability or probability.by using this
significant positions are identified in order tothem. The other analytic attributes
, which in the HODM areCombing, Plaiting and Colouring
explained in following subsections
Copyright © 2014 Praise Worthy Prize S.r.l.
Direction is equal to Position and‘necessary
As can be seendata values of two positions as an instance.
(Buoy2)based on the structure through the save spatial data in arow and temporal data in a nested table
3.emphasized that
the hairparticular position specifications.
Proposed
his paper focuses on definingthree analytical functions called
ing. These functions are usedclassification or clustering methods
natural specification of hair.involve the proposition of, but, we propose a new way
data modelling and representation based on newstructural attributes by embedding analysis functions.
allow us tosponse time. It is possible to
process analytical findings by changing or recording, which are
The importance of the values that are allocated to athe
attribute can be declared in numeric form as a weight orstring form as a fuzzy value base
used for reporting, querto data reliability or probability.
Strengthsignificant positions are identified in order tothem. The other analytic attributes
, which in the HODM areCombing, Plaiting and Colouring
subsections
Copyright © 2014 Praise Worthy Prize S.r.l.
Direction is equal to Position andecessary
As can be seendata values of two positions as an instance.
isbased on the structure through the save spatial data in arow and temporal data in a nested table
emphasized that thethe hair
particular position specifications.
Proposed A
his paper focuses on definingcalled
ing. These functions are usedclassification or clustering methods,
natural specification of hair.involve the proposition of
rather use available, we propose a new way
data modelling and representation based on newstructural attributes by embedding analysis functions.
allow us tosponse time. It is possible to
process analytical findings by changing or recording, which are
The importance of the values that are allocated to aStrength
attribute can be declared in numeric form as a weight orstring form as a fuzzy value base
used for reporting, querying,to data reliability or probability.
Strengthsignificant positions are identified in order tothem. The other analytic attributes
, which in the HODM areCombing, Plaiting and Colouring
subsections
A. Madraky, Z. A. Othman, A. R. Hamdan
- All rights reserved
Fig. 3. El Nino Subdata in HODM for two positions
Direction is equal to Position andecessary
As can be seendata values of two positions as an instance.
defined in thebased on the structure through the save spatial data in arow and temporal data in a nested table
the two positions aretable and each row
particular position specifications.
Analytical
his paper focuses on definingcalled Combing, Plaiting
ing. These functions are usedand their features are
natural specification of hair.involve the proposition of
rather use available, we propose a new way
data modelling and representation based on newstructural attributes by embedding analysis functions.
allow us to expand the querysponse time. It is possible to
process analytical findings by changing or recording, which are Strength, Direction,
The importance of the values that are allocated to aStrength
attribute can be declared in numeric form as a weight orstring form as a fuzzy value based
ying,to data reliability or probability.
Strength attribute,significant positions are identified in order tothem. The other analytic attributes
, which in the HODM areCombing, Plaiting and Colouring
subsections.
A. Madraky, Z. A. Othman, A. R. Hamdan
All rights reserved
Fig. 3. El Nino Subdata in HODM for two positions
Direction is equal to Position andecessary’ due to position
As can be seen fromdata values of two positions as an instance.
defined in thebased on the structure through the save spatial data in arow and temporal data in a nested table called
two positions aretable and each row
particular position specifications.
nalytical
his paper focuses on definingCombing, Plaiting
ing. These functions are usedand their features are
natural specification of hair.involve the proposition of
rather use available, we propose a new way
data modelling and representation based on newstructural attributes by embedding analysis functions.
expand the querysponse time. It is possible to
process analytical findings by changing or recordingStrength, Direction,
The importance of the values that are allocated to aStrength
attribute can be declared in numeric form as a weight oron the processingand
to data reliability or probability.attribute,
significant positions are identified in order tothem. The other analytic attributes are
, which in the HODM areCombing, Plaiting and Colouring functio
A. Madraky, Z. A. Othman, A. R. Hamdan
All rights reserved
Fig. 3. El Nino Subdata in HODM for two positions
Direction is equal to Position anddue to position
from Tabledata values of two positions as an instance.
defined in thebased on the structure through the save spatial data in a
called
two positions aretable and each row
nalytical
his paper focuses on definingCombing, Plaiting
ing. These functions are used to representand their features are
natural specification of hair.involve the proposition of
rather use available, we propose a new way
data modelling and representation based on newstructural attributes by embedding analysis functions.
expand the querysponse time. It is possible to
process analytical findings by changing or recordingStrength, Direction,
The importance of the values that are allocated to aattribute. This
attribute can be declared in numeric form as a weight oron the processingand also defining
to data reliability or probability.attribute,
significant positions are identified in order toare
, which in the HODM arefunctio
A. Madraky, Z. A. Othman, A. R. Hamdan
All rights reserved
Fig. 3. El Nino Subdata in HODM for two positions
Direction is equal to Position anddue to position
Tabledata values of two positions as an instance.
defined in thebased on the structure through the save spatial data in a
called Cell
two positions aretable and each row
nalytical Functions
his paper focuses on definingCombing, Plaiting
to representand their features are
natural specification of hair.involve the proposition of
rather use available, we propose a new way
data modelling and representation based on newstructural attributes by embedding analysis functions.
expand the querysponse time. It is possible to
process analytical findings by changing or recordingStrength, Direction,
The importance of the values that are allocated to aattribute. This
attribute can be declared in numeric form as a weight oron the processing
also definingto data reliability or probability.
attribute, the nonsignificant positions are identified in order to
Direction,, which in the HODM are dealt with
functions, which
A. Madraky, Z. A. Othman, A. R. Hamdan
All rights reserved
Fig. 3. El Nino Subdata in HODM for two positions
Direction is equal to Position anddue to position
Table IIdata values of two positions as an instance.
defined in the modelbased on the structure through the save spatial data in a
Cell.
two positions aretable and each row
unctions
his paper focuses on definingCombing, Plaiting
to representand their features are
natural specification of hair.involve the proposition of
rather use available, we propose a new way
data modelling and representation based on newstructural attributes by embedding analysis functions.
expand the querysponse time. It is possible to
process analytical findings by changing or recordingStrength, Direction,
The importance of the values that are allocated to aattribute. This
attribute can be declared in numeric form as a weight oron the processing
also definingto data reliability or probability.
the nonremove
Direction,dealt withns, which
A. Madraky, Z. A. Othman, A. R. Hamdan
Fig. 3. El Nino Subdata in HODM for two positions
Direction is equal to Position anddue to position
II, wedata values of two positions as an instance.
modelbased on the structure through the save spatial data in a
The
two positions aretable and each row
unctions
his paper focuses on definingCombing, Plaiting
to representand their features are
involve the proposition of newrather use available
, we propose a new way todata modelling and representation based on new
structural attributes by embedding analysis functions.expand the query
sponse time. It is possible toprocess analytical findings by changing or recording
Strength, Direction,
The importance of the values that are allocated to aattribute. This
attribute can be declared in numeric form as a weight oron the processing
also definingto data reliability or probability.
the nonremove
Direction,dealt withns, which
A. Madraky, Z. A. Othman, A. R. Hamdan
552
Fig. 3. El Nino Subdata in HODM for two positions
Direction is equal to Position anddue to position
, we
modelbased on the structure through the save spatial data in a
The
two positions aretable and each row
unctions
his paper focuses on definingCombing, Plaiting
to representand their features are
newrather use available
todata modelling and representation based on new
expand the querysponse time. It is possible to
process analytical findings by changing or recordingStrength, Direction,
The importance of the values that are allocated to aattribute. This
attribute can be declared in numeric form as a weight oron the processing
also definingto data reliability or probability.
the non-remove
Direction,dealt withns, which
A. Madraky, Z. A. Othman, A. R. Hamdan
552
Fig. 3. El Nino Subdata in HODM for two positions
a set of data about a position. Direction or orientationusedby a positionof a related data set is stored in an attributePositionattribute is initialized by
closeddefine data similarity or relationships in fuzzy formbecausea specific radius. The formal definition of this functionas follows:
positionas an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitationhairmore similarity with
byoutput. The default value forequal tohair is not directed so it is not assigned to any class.
inPPositionX, PPositionY as posPDirection as a new value for direction. As mentionedaboveDirection value and it
other wordposition which we have not any data related to itillustrates theanalytic values
A. Madraky, Z. A. Othman, A. R. Hamdan
Fig. 3. El Nino Subdata in HODM for two positions
This function sets the data direction or orientation fora set of data about a position. Direction or orientationusedby a positionof a related data set is stored in an attributePositionattribute is initialized by
We assign some data to a classcloseddefine data similarity or relationships in fuzzy formbecausea specific radius. The formal definition of this functionas follows:
Inpositionas an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitationhairmore similarity with
Weby this function so we can use a set of hair for input oroutput. The default value forequal tohair is not directed so it is not assigned to any class.
We showin Fig. 4PPositionX, PPositionY as posPDirection as a new value for direction. As mentionedaboveDirection value and it
This valueother wordposition which we have not any data related to itillustrates theanalytic values
A. Madraky, Z. A. Othman, A. R. Hamdan
Fig. 3. El Nino Subdata in HODM for two positions
This function sets the data direction or orientation fora set of data about a position. Direction or orientationused to representby a positionof a related data set is stored in an attributePositionattribute is initialized by
We assign some data to a classcloseddefine data similarity or relationships in fuzzy formbecausea specific radius. The formal definition of this functionas follows:
H Former _ Direction H New _ Direction
In formulapositionas an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitationhair directionmore similarity with
Wethis function so we can use a set of hair for input or
output. The default value forequal tohair is not directed so it is not assigned to any class.
We showFig. 4
PPositionX, PPositionY as posPDirection as a new value for direction. As mentionedabove,Direction value and it
This valueother wordposition which we have not any data related to itillustrates theanalytic values
A. Madraky, Z. A. Othman, A. R. Hamdan
International Review on Computers and Software, Vol. 9, N. 3
Fig. 3. El Nino Subdata in HODM for two positions
This function sets the data direction or orientation fora set of data about a position. Direction or orientation
to representby a positionof a related data set is stored in an attributePosition, as has noted inattribute is initialized by
We assign some data to a classdirections. As can be deduced, it is possible to
define data similarity or relationships in fuzzy formbecause thea specific radius. The formal definition of this functionas follows:
H Former _ Direction H New_ Direction
formulaposition calledas an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitation
directionmore similarity with
canthis function so we can use a set of hair for input or
output. The default value forequal to that of thehair is not directed so it is not assigned to any class.
We showFig. 4.
PPositionX, PPositionY as posPDirection as a new value for direction. As mentioned
theDirection value and it
This valueother wordposition which we have not any data related to itillustrates theanalytic values
A. Madraky, Z. A. Othman, A. R. Hamdan
International Review on Computers and Software, Vol. 9, N. 3
Fig. 3. El Nino Subdata in HODM for two positions
This function sets the data direction or orientation fora set of data about a position. Direction or orientation
to representby a position’sof a related data set is stored in an attribute
as has noted inattribute is initialized by
We assign some data to a classdirections. As can be deduced, it is possible to
define data similarity or relationships in fuzzy formthe direction
a specific radius. The formal definition of this functionas follows:
H Former _ Direction H New _ Direction
formulacalled
as an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitation
direction,more similarity with
can assignthis function so we can use a set of hair for input or
output. The default value forthat of the
hair is not directed so it is not assigned to any class.We showed
. The input arguments of this function arePPositionX, PPositionY as posPDirection as a new value for direction. As mentioned
the coordinates of a point are used forDirection value and it
This valueother words, Direction values canposition which we have not any data related to itillustrates theanalytic values
A. Madraky, Z. A. Othman, A. R. Hamdan
International Review on Computers and Software, Vol. 9, N. 3
Fig. 3. El Nino Subdata in HODM for two positions
This function sets the data direction or orientation fora set of data about a position. Direction or orientation
to represent’s coordinates.
of a related data set is stored in an attributeas has noted in
attribute is initialized byWe assign some data to a class
directions. As can be deduced, it is possible todefine data similarity or relationships in fuzzy form
directiona specific radius. The formal definition of this function
H Former _ Direction H New _ Direction
(1),called ‘Hair
as an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitation
, but it is better to select adjacent objects formore similarity with
assignthis function so we can use a set of hair for input or
output. The default value forthat of the
hair is not directed so it is not assigned to any class.ed the Combing function by SQL statementsThe input arguments of this function are
PPositionX, PPositionY as posPDirection as a new value for direction. As mentioned
coordinates of a point are used forDirection value and it
This value can, Direction values can
position which we have not any data related to itillustrates the effects of the canalytic values.
A. Madraky, Z. A. Othman, A. R. Hamdan
International Review on Computers and Software, Vol. 9, N. 3
Fig. 3. El Nino Subdata in HODM for two positions
This function sets the data direction or orientation fora set of data about a position. Direction or orientation
to represent objects in a class and it is determinedcoordinates.
of a related data set is stored in an attributeas has noted in
attribute is initialized byWe assign some data to a class
directions. As can be deduced, it is possible todefine data similarity or relationships in fuzzy form
directiona specific radius. The formal definition of this function
H Former _ Direction H New _ Direction
(1), HHair
as an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitation
but it is better to select adjacent objects formore similarity with
assign the common direction tothis function so we can use a set of hair for input or
output. The default value forthat of the
hair is not directed so it is not assigned to any class.the Combing function by SQL statements
The input arguments of this function arePPositionX, PPositionY as posPDirection as a new value for direction. As mentioned
coordinates of a point are used forDirection value and it
can be different from data positions. In, Direction values can
position which we have not any data related to iteffects of the c
International Review on Computers and Software, Vol. 9, N. 3
III.2.
This function sets the data direction or orientation fora set of data about a position. Direction or orientation
objects in a class and it is determinedcoordinates.
of a related data set is stored in an attributeas has noted in
attribute is initialized byWe assign some data to a class
directions. As can be deduced, it is possible todefine data similarity or relationships in fuzzy form
direction could be considereda specific radius. The formal definition of this function
Combing :
H Former _ Direction H New_ Direction
is defined as a set of related data to aHair’ in
as an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitation
but it is better to select adjacent objects forthe
the common direction tothis function so we can use a set of hair for input or
output. The default value forPosition attribute. In this case, the
hair is not directed so it is not assigned to any class.the Combing function by SQL statements
The input arguments of this function arePPositionX, PPositionY as posPDirection as a new value for direction. As mentioned
coordinates of a point are used forDirection value and it could
be different from data positions. In, Direction values can
position which we have not any data related to iteffects of the c
International Review on Computers and Software, Vol. 9, N. 3
III.2.
This function sets the data direction or orientation fora set of data about a position. Direction or orientation
objects in a class and it is determinedcoordinates.
of a related data set is stored in an attributeas has noted in [
attribute is initialized by theWe assign some data to a class
directions. As can be deduced, it is possible todefine data similarity or relationships in fuzzy form
could be considereda specific radius. The formal definition of this function
Combing :
H Former _ Direction H New _ Direction
is defined as a set of related data to ain the
as an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitation
but it is better to select adjacent objects forthe natural model.
the common direction tothis function so we can use a set of hair for input or
output. The default value forPosition attribute. In this case, the
hair is not directed so it is not assigned to any class.the Combing function by SQL statements
The input arguments of this function arePPositionX, PPositionY as posPDirection as a new value for direction. As mentioned
coordinates of a point are used forcould
be different from data positions. In, Direction values can
position which we have not any data related to iteffects of the c
International Review on Computers and Software, Vol. 9, N. 3
Combing
This function sets the data direction or orientation fora set of data about a position. Direction or orientation
objects in a class and it is determinedcoordinates. In the
of a related data set is stored in an attribute[44]
the combing function.We assign some data to a class
directions. As can be deduced, it is possible todefine data similarity or relationships in fuzzy form
could be considereda specific radius. The formal definition of this function
Combing :
H Former _ Direction H New_ Direction
is defined as a set of related data to athe HODM and Direction is used
as an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitation
but it is better to select adjacent objects fornatural model.
the common direction tothis function so we can use a set of hair for input or
output. The default value forPosition attribute. In this case, the
hair is not directed so it is not assigned to any class.the Combing function by SQL statements
The input arguments of this function arePPositionX, PPositionY as posPDirection as a new value for direction. As mentioned
coordinates of a point are used forcould determine a
be different from data positions. In, Direction values can
position which we have not any data related to iteffects of the c
International Review on Computers and Software, Vol. 9, N. 3
Combing
This function sets the data direction or orientation fora set of data about a position. Direction or orientation
objects in a class and it is determinedIn the
of a related data set is stored in an attribute]. Direction as an analyticombing function.
We assign some data to a classdirections. As can be deduced, it is possible to
define data similarity or relationships in fuzzy formcould be considered
a specific radius. The formal definition of this function
Combing :
H Former _ Direction H New _ Direction
is defined as a set of related data to aHODM and Direction is used
as an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitation
but it is better to select adjacent objects fornatural model.
the common direction tothis function so we can use a set of hair for input or
output. The default value for thePosition attribute. In this case, the
hair is not directed so it is not assigned to any class.the Combing function by SQL statements
The input arguments of this function arePPositionX, PPositionY as posPDirection as a new value for direction. As mentioned
coordinates of a point are used fordetermine a
be different from data positions. In, Direction values can
position which we have not any data related to iteffects of the combing function
International Review on Computers and Software, Vol. 9, N. 3
Combing
This function sets the data direction or orientation fora set of data about a position. Direction or orientation
objects in a class and it is determinedIn the HODM,
of a related data set is stored in an attribute. Direction as an analytic
ombing function.We assign some data to a class
directions. As can be deduced, it is possible todefine data similarity or relationships in fuzzy form
could be considereda specific radius. The formal definition of this function
Combing :
H Former _ Direction H New_ Direction
is defined as a set of related data to aHODM and Direction is used
as an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitation
but it is better to select adjacent objects fornatural model.
the common direction tothis function so we can use a set of hair for input or
the Direction attPosition attribute. In this case, the
hair is not directed so it is not assigned to any class.the Combing function by SQL statements
The input arguments of this function arePPositionX, PPositionY as posPDirection as a new value for direction. As mentioned
coordinates of a point are used fordetermine a
be different from data positions. In, Direction values can
position which we have not any data related to itombing function
International Review on Computers and Software, Vol. 9, N. 3
This function sets the data direction or orientation fora set of data about a position. Direction or orientation
objects in a class and it is determinedHODM,
of a related data set is stored in an attribute. Direction as an analytic
ombing function.We assign some data to a class with
directions. As can be deduced, it is possible todefine data similarity or relationships in fuzzy form
could be considereda specific radius. The formal definition of this function
H Former _ Direction H New _ Direction
is defined as a set of related data to aHODM and Direction is used
as an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitation
but it is better to select adjacent objects fornatural model.
the common direction tothis function so we can use a set of hair for input or
Direction attPosition attribute. In this case, the
hair is not directed so it is not assigned to any class.the Combing function by SQL statements
The input arguments of this function arePPositionX, PPositionY as position values andPDirection as a new value for direction. As mentioned
coordinates of a point are used fordetermine an area
be different from data positions. In, Direction values can be pointed to
position which we have not any data related to itombing function
International Review on Computers and Software, Vol. 9, N. 3
This function sets the data direction or orientation fora set of data about a position. Direction or orientation
objects in a class and it is determinedHODM,
of a related data set is stored in an attribute. Direction as an analytic
ombing function.with
directions. As can be deduced, it is possible todefine data similarity or relationships in fuzzy form
could be considered as a zone witha specific radius. The formal definition of this function
H Former _ Direction H New _ Direction
is defined as a set of related data to aHODM and Direction is used
as an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitation
but it is better to select adjacent objects for
the common direction tothis function so we can use a set of hair for input or
Direction attPosition attribute. In this case, the
hair is not directed so it is not assigned to any class.the Combing function by SQL statements
The input arguments of this function areition values and
PDirection as a new value for direction. As mentionedcoordinates of a point are used for
n areabe different from data positions. In
be pointed toposition which we have not any data related to it
ombing function
International Review on Computers and Software, Vol. 9, N. 3
This function sets the data direction or orientation fora set of data about a position. Direction or orientation
objects in a class and it is determinedHODM, the
of a related data set is stored in an attribute. Direction as an analytic
ombing function.the same or
directions. As can be deduced, it is possible todefine data similarity or relationships in fuzzy form
as a zone witha specific radius. The formal definition of this function
H Former _ Direction H New_ Direction
is defined as a set of related data to aHODM and Direction is used
as an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitation
but it is better to select adjacent objects for
the common direction to a set of hairthis function so we can use a set of hair for input or
Direction attPosition attribute. In this case, the
hair is not directed so it is not assigned to any class.the Combing function by SQL statements
The input arguments of this function areition values and
PDirection as a new value for direction. As mentionedcoordinates of a point are used for
n area of hair.be different from data positions. In
be pointed toposition which we have not any data related to it
ombing function
International Review on Computers and Software, Vol. 9, N. 3
This function sets the data direction or orientation fora set of data about a position. Direction or orientation
objects in a class and it is determinedthe location
of a related data set is stored in an attribute. Direction as an analytic
the same ordirections. As can be deduced, it is possible to
define data similarity or relationships in fuzzy formas a zone with
a specific radius. The formal definition of this function
H Former _ Direction H New _ Direction
is defined as a set of related data to aHODM and Direction is used
as an attribute of it. Combing assigns a new valueattribute. In the definition, there is no limitation
but it is better to select adjacent objects for
a set of hairthis function so we can use a set of hair for input or
Direction attribute isPosition attribute. In this case, the
hair is not directed so it is not assigned to any class.the Combing function by SQL statements
The input arguments of this function areition values and
PDirection as a new value for direction. As mentionedcoordinates of a point are used for
of hair.be different from data positions. In
be pointed toposition which we have not any data related to it.
ombing function
International Review on Computers and Software, Vol. 9, N. 3
This function sets the data direction or orientation fora set of data about a position. Direction or orientation
objects in a class and it is determinedlocation
called. Direction as an analytic
the same ordirections. As can be deduced, it is possible to
define data similarity or relationships in fuzzy formas a zone with
a specific radius. The formal definition of this function
H Former _ Direction H New _ Direction
is defined as a set of related data to aHODM and Direction is used
toon
but it is better to select adjacent objects for
a set of hairthis function so we can use a set of hair for input or
ribute isPosition attribute. In this case, the
hair is not directed so it is not assigned to any class.the Combing function by SQL statements
The input arguments of this function areition values and
PDirection as a new value for direction. As mentionedcoordinates of a point are used for
of hair.be different from data positions. In
be pointed toFig. 5
on
International Review on Computers and Software, Vol. 9, N. 3
This function sets the data direction or orientation fora set of data about a position. Direction or orientation is
objects in a class and it is determinedlocation
called. Direction as an analytic
the same ordirections. As can be deduced, it is possible to
define data similarity or relationships in fuzzy formas a zone with
a specific radius. The formal definition of this function is
(1)
is defined as a set of related data to aHODM and Direction is used
thisthe
but it is better to select adjacent objects for
a set of hairthis function so we can use a set of hair for input or
ribute isPosition attribute. In this case, the
the Combing function by SQL statementsThe input arguments of this function are
ition values andPDirection as a new value for direction. As mentioned
the
be different from data positions. Inbe pointed to a
Fig. 5the
International Review on Computers and Software, Vol. 9, N. 3
This function sets the data direction or orientation foris
objects in a class and it is determinedlocation
called. Direction as an analytic
the same ordirections. As can be deduced, it is possible to
define data similarity or relationships in fuzzy formas a zone with
is
(1)
is defined as a set of related data to aHODM and Direction is used
thisthe
but it is better to select adjacent objects for
a set of hairthis function so we can use a set of hair for input or
ribute isPosition attribute. In this case, the
the Combing function by SQL statementsThe input arguments of this function are
ition values andPDirection as a new value for direction. As mentioned
the
be different from data positions. Ina
Fig. 5the
Copyright © 2014 Praise Worthy Prize S.r.l.
to Buoy2 so that the two positions are assigned tosame class. Note thatis done byfunction is used for output representationattributequeries.Direction attribute of Buoy1coordinateit tofunction. However,arefollowing subsections
theterms of
than 8 are directed to the specific position for datagrouping’.
HODMfunction assigns a set of adjacent hgroup.
AS
Copyright © 2014 Praise Worthy Prize S.r.l.
It showsto Buoy2 so that the two positions are assigned tosame class. Note thatis done byfunction is used for output representationattributequeries.Direction attribute of Buoy1coordinateit tofunction. However,are alsofollowing subsections
Therefore, the Directionthe Position values.terms of
For example, ‘All positions withthan 8 are directed to the specific position for datagrouping’.
PlaitingHODMfunction assigns a set of adjacent hgroup.
CREATE PROCEDURE HM.COMBING (
)AS/* TYPE LOCAL VARIABLES DECLARATION HERE */BEGINUPDATE HM.HAIRSET DIRECTION=PDIRECTIONWHERE POSITIONX=PPOSITIONX AND
POSITIONY=PPOSITIONYRETURN;
END;/
Copyright © 2014 Praise Worthy Prize S.r.l.
It showsto Buoy2 so that the two positions are assigned tosame class. Note thatis done byfunction is used for output representationattributequeries. According toDirection attribute of Buoy1coordinate
thefunction. However,
alsofollowing subsections
Therefore, the DirectionPosition values.
terms of aFor example, ‘All positions with
than 8 are directed to the specific position for datagrouping’.
PlaitingHODMfunction assigns a set of adjacent hgroup. It
CREATE PROCEDURE HM.COMBING (PPOSITIONX IN NUMBER,PPOSITIONY IN NUMBER,PDIRECTION IN HM.POINTTYPE
/* TYPE LOCAL VARIABLES DECLARATION HERE */BEGINUPDATE HM.HAIRSET DIRECTION=PDIRECTIONWHERE POSITIONX=PPOSITIONX AND
POSITIONY=PPOSITIONYRETURN;
END;
Copyright © 2014 Praise Worthy Prize S.r.l.
Fig. 4. SQL statements for
It shows howto Buoy2 so that the two positions are assigned tosame class. Note thatis done byfunction is used for output representation
but this attribute is applied for performingAccording to
Direction attribute of Buoy1coordinate, i.e. the
coordinate of Buoy2 byfunction. However,
changedfollowing subsections
Therefore, the DirectionPosition values.
a data grouping based on specified conditions.For example, ‘All positions with
than 8 are directed to the specific position for datagrouping’.
Plaiting is utilized as another analytic function infor clustering representation.
function assigns a set of adjacent hIt is
CREATE PROCEDURE HM.COMBING (PPOSITIONX IN NUMBER,PPOSITIONY IN NUMBER,PDIRECTION IN HM.POINTTYPE
/* TYPE LOCAL VARIABLES DECLARATION HERE */
UPDATE HM.HAIRSET DIRECTION=PDIRECTIONWHERE POSITIONX=PPOSITIONX AND
POSITIONY=PPOSITIONYRETURN;
Copyright © 2014 Praise Worthy Prize S.r.l.
Fig. 4. SQL statements for
howto Buoy2 so that the two positions are assigned tosame class. Note that
thefunction is used for output representation
but this attribute is applied for performingAccording to
Direction attribute of Buoy1, i.e. thecoordinate of Buoy2 by
function. However,changed
following subsectionsTherefore, the Direction
Position values.data grouping based on specified conditions.
For example, ‘All positions withthan 8 are directed to the specific position for data
is utilized as another analytic function infor clustering representation.
function assigns a set of adjacent halso used
CREATE PROCEDURE HM.COMBING (PPOSITIONX IN NUMBER,PPOSITIONY IN NUMBER,PDIRECTION IN HM.POINTTYPE
/* TYPE LOCAL VARIABLES DECLARATION HERE */
UPDATE HM.HAIRSET DIRECTION=PDIRECTIONWHERE POSITIONX=PPOSITIONX AND
POSITIONY=PPOSITIONYRETURN;
Copyright © 2014 Praise Worthy Prize S.r.l.
Fig. 4. SQL statements for
how Direction changto Buoy2 so that the two positions are assigned tosame class. Note that
the relevant algorithms andfunction is used for output representation
but this attribute is applied for performingAccording to
Direction attribute of Buoy1, i.e. thecoordinate of Buoy2 by
function. However,changed;
following subsectionsTherefore, the Direction
Position values.data grouping based on specified conditions.
For example, ‘All positions withthan 8 are directed to the specific position for data
is utilized as another analytic function infor clustering representation.
function assigns a set of adjacent halso used
CREATE PROCEDURE HM.COMBING (PPOSITIONX IN NUMBER,PPOSITIONY IN NUMBER,PDIRECTION IN HM.POINTTYPE
/* TYPE LOCAL VARIABLES DECLARATION HERE */
UPDATE HM.HAIRSET DIRECTION=PDIRECTIONWHERE POSITIONX=PPOSITIONX AND
POSITIONY=PPOSITIONY
Copyright © 2014 Praise Worthy Prize S.r.l.
Fig. 4. SQL statements for
Direction changto Buoy2 so that the two positions are assigned tosame class. Note that,
relevant algorithms andfunction is used for output representation
but this attribute is applied for performingAccording to the
Direction attribute of Buoy1default value. It is possible to change
coordinate of Buoy2 byfunction. However, in
these changes arefollowing subsections.
Therefore, the DirectionPosition values. The
data grouping based on specified conditions.For example, ‘All positions with
than 8 are directed to the specific position for data
III.3.
is utilized as another analytic function infor clustering representation.
function assigns a set of adjacent halso used
CREATE PROCEDURE HM.COMBING (PPOSITIONX IN NUMBER,PPOSITIONY IN NUMBER,PDIRECTION IN HM.POINTTYPE
/* TYPE LOCAL VARIABLES DECLARATION HERE */
UPDATE HM.HAIRSET DIRECTION=PDIRECTIONWHERE POSITIONX=PPOSITIONX AND
POSITIONY=PPOSITIONY
Copyright © 2014 Praise Worthy Prize S.r.l.
Fig. 4. SQL statements for
Direction changto Buoy2 so that the two positions are assigned to
as mentionedrelevant algorithms and
function is used for output representationbut this attribute is applied for performing
theDirection attribute of Buoy1
default value. It is possible to changecoordinate of Buoy2 by
doing so,these changes are
Therefore, the DirectionThe
data grouping based on specified conditions.For example, ‘All positions with
than 8 are directed to the specific position for data
III.3.
is utilized as another analytic function infor clustering representation.
function assigns a set of adjacent halso used as a classification method to
CREATE PROCEDURE HM.COMBING (PPOSITIONX IN NUMBER,PPOSITIONY IN NUMBER,PDIRECTION IN HM.POINTTYPE
/* TYPE LOCAL VARIABLES DECLARATION HERE */
SET DIRECTION=PDIRECTIONWHERE POSITIONX=PPOSITIONX AND
POSITIONY=PPOSITIONY
Copyright © 2014 Praise Worthy Prize S.r.l.
Fig. 4. SQL statements for
Direction changto Buoy2 so that the two positions are assigned to
as mentionedrelevant algorithms and
function is used for output representationbut this attribute is applied for performing
data values in thDirection attribute of Buoy1
default value. It is possible to changecoordinate of Buoy2 by
doing so,these changes are
Therefore, the Direction valueDirection value is assigned in
data grouping based on specified conditions.For example, ‘All positions with
than 8 are directed to the specific position for data
Plaiting
is utilized as another analytic function infor clustering representation.
function assigns a set of adjacent has a classification method to
CREATE PROCEDURE HM.COMBING (PPOSITIONX IN NUMBER,PPOSITIONY IN NUMBER,PDIRECTION IN HM.POINTTYPE
/* TYPE LOCAL VARIABLES DECLARATION HERE */
SET DIRECTION=PDIRECTIONWHERE POSITIONX=PPOSITIONX AND
POSITIONY=PPOSITIONY
Copyright © 2014 Praise Worthy Prize S.r.l.
Fig. 4. SQL statements for C
Direction changto Buoy2 so that the two positions are assigned to
as mentionedrelevant algorithms and
function is used for output representationbut this attribute is applied for performing
data values in thDirection attribute of Buoy1 is
default value. It is possible to changecoordinate of Buoy2 by
doing so,these changes are
valueDirection value is assigned in
data grouping based on specified conditions.For example, ‘All positions with
than 8 are directed to the specific position for data
Plaiting
is utilized as another analytic function infor clustering representation.
function assigns a set of adjacent has a classification method to
CREATE PROCEDURE HM.COMBING (PPOSITIONX IN NUMBER,PPOSITIONY IN NUMBER,PDIRECTION IN HM.POINTTYPE
/* TYPE LOCAL VARIABLES DECLARATION HERE */
SET DIRECTION=PDIRECTIONWHERE POSITIONX=PPOSITIONX AND
Copyright © 2014 Praise Worthy Prize S.r.l.
Combing
Direction changesto Buoy2 so that the two positions are assigned to
as mentionedrelevant algorithms and
function is used for output representationbut this attribute is applied for performing
data values in this equal to
default value. It is possible to changecoordinate of Buoy2 by
other analytic attributesthese changes are
value may be different fromDirection value is assigned in
data grouping based on specified conditions.For example, ‘All positions with
than 8 are directed to the specific position for data
Plaiting
is utilized as another analytic function infor clustering representation.
function assigns a set of adjacent hairs or positions into aas a classification method to
CREATE PROCEDURE HM.COMBING (
PDIRECTION IN HM.POINTTYPE
/* TYPE LOCAL VARIABLES DECLARATION HERE */
SET DIRECTION=PDIRECTIONWHERE POSITIONX=PPOSITIONX AND
A. Madraky, Z. A. Othman, A. R. Hamdan
- All rights reserved
ombing
from position Buoy1to Buoy2 so that the two positions are assigned to
as mentioned aboverelevant algorithms and
function is used for output representationbut this attribute is applied for performing
data values in thequal to
default value. It is possible to changecoordinate of Buoy2 by using
other analytic attributesthese changes are
may be different fromDirection value is assigned in
data grouping based on specified conditions.For example, ‘All positions with a longitude
than 8 are directed to the specific position for data
Plaiting
is utilized as another analytic function infor clustering representation.
airs or positions into aas a classification method to
Fig.
CREATE PROCEDURE HM.COMBING (
PDIRECTION IN HM.POINTTYPE
/* TYPE LOCAL VARIABLES DECLARATION HERE */
WHERE POSITIONX=PPOSITIONX AND
A. Madraky, Z. A. Othman, A. R. Hamdan
All rights reserved
ombing method
from position Buoy1to Buoy2 so that the two positions are assigned to
aboverelevant algorithms and
function is used for output representation ofbut this attribute is applied for performing
data values in thequal to
default value. It is possible to changeusing
other analytic attributesexplain
may be different fromDirection value is assigned in
data grouping based on specified conditions.longitude
than 8 are directed to the specific position for data
is utilized as another analytic function infor clustering representation.
airs or positions into aas a classification method to
Fig. 5.
/* TYPE LOCAL VARIABLES DECLARATION HERE */
WHERE POSITIONX=PPOSITIONX AND
A. Madraky, Z. A. Othman, A. R. Hamdan
All rights reserved
ethod
from position Buoy1to Buoy2 so that the two positions are assigned to
above, classificationrelevant algorithms and the c
of thebut this attribute is applied for performing
data values in the example,equal to the
default value. It is possible to changeusing the c
other analytic attributesexplain
may be different fromDirection value is assigned in
data grouping based on specified conditions.longitude
than 8 are directed to the specific position for data
is utilized as another analytic function inThe p
airs or positions into aas a classification method to
El Nino Sub
/* TYPE LOCAL VARIABLES DECLARATION HERE */
A. Madraky, Z. A. Othman, A. R. Hamdan
All rights reserved
ethod
from position Buoy1to Buoy2 so that the two positions are assigned to
, classificationthe cthe Direction
but this attribute is applied for performingexample,the
default value. It is possible to changethe c
other analytic attributesexplained
may be different fromDirection value is assigned in
data grouping based on specified conditions.longitude
than 8 are directed to the specific position for data
is utilized as another analytic function inThe p
airs or positions into aas a classification method to
El Nino Sub
/* TYPE LOCAL VARIABLES DECLARATION HERE */
A. Madraky, Z. A. Othman, A. R. Hamdan
All rights reserved
from position Buoy1to Buoy2 so that the two positions are assigned to
, classificationthe combing
Directionbut this attribute is applied for performing
example,position
default value. It is possible to changethe combing
other analytic attributesed in
may be different fromDirection value is assigned in
data grouping based on specified conditions.of more
than 8 are directed to the specific position for data
is utilized as another analytic function inThe plaiting
airs or positions into aas a classification method to
El Nino Sub
/* TYPE LOCAL VARIABLES DECLARATION HERE */
A. Madraky, Z. A. Othman, A. R. Hamdan
from position Buoy1to Buoy2 so that the two positions are assigned to the
, classificationombing
Directionbut this attribute is applied for performing
example, theposition
default value. It is possible to changeombing
other analytic attributesin the
may be different fromDirection value is assigned in
data grouping based on specified conditions.more
than 8 are directed to the specific position for data
is utilized as another analytic function in thelaiting
airs or positions into aas a classification method to
El Nino Subdata in HODM (
/* TYPE LOCAL VARIABLES DECLARATION HERE */
A. Madraky, Z. A. Othman, A. R. Hamdan
553
from position Buoy1the
, classificationombing
Directionbut this attribute is applied for performing
theposition
default value. It is possible to changeombing
other analytic attributesthe
may be different fromDirection value is assigned in
morethan 8 are directed to the specific position for data
thelaiting
airs or positions into aas a classification method to
ata in HODM (
A. Madraky, Z. A. Othman, A. R. Hamdan
553
represent the results of thealgorithmspecifying membership in a group. This attributedetermines and saveof hair names.their positions, butvicinity radius. The default
the pspecificationassignedisthe edges ofso we can define an area by determining the borderpointsfollows:
without using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, wehave to save multiple linkeproposed function, we considered one linked list to avoidaddingare shown in Fig. 6.Proximity values by determiningsimibeing that pc
Buoy7dAspositions, but at least three positions should be listedbecause tha zone.
ata in HODM (
A. Madraky, Z. A. Othman, A. R. Hamdan
represent the results of thealgorithmspecifying membership in a group. This attributedetermines and saveof hair names.their positions, butvicinity radius. The default
Therefore, a hairthe pspecificationassignedis assumed by border positions so it is enough to specifythe edges ofso we can define an area by determining the borderpointsfollows:
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, wehave to save multiple linkeproposed function, we considered one linked list to avoidaddingare shown in Fig. 6.Proximity values by determiningsimibeing that pcombing define
In Fig.Buoy7determine an area for specifying some adjacent positions.Aspositions, but at least three positions should be listedbecause tha zone.
ata in HODM (d
A. Madraky, Z. A. Othman, A. R. Hamdan
represent the results of thealgorithmspecifying membership in a group. This attributedetermines and saveof hair names.their positions, butvicinity radius. The default
Therefore, a hairthe plaiting functionspecificationassigned
assumed by border positions so it is enough to specifythe edges ofso we can define an area by determining the borderpoints. The formal definition of this functionfollows:
H Former _ Proximity H New_ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, wehave to save multiple linkeproposed function, we considered one linked list to avoidaddingare shown in Fig. 6.Proximity values by determiningsimilarbeing that pombing define
In Fig.Buoy7, which are neighbour
etermine an area for specifying some adjacent positions.mentioned above
positions, but at least three positions should be listedbecause tha zone.
direction is updated)
A. Madraky, Z. A. Othman, A. R. Hamdan
International Review on Computers and Software, Vol. 9, N. 3
represent the results of thealgorithm. The Proximity attribute is definedspecifying membership in a group. This attributedetermines and saveof hair names.their positions, butvicinity radius. The default
Therefore, a hairlaiting function
specificationassigned to
assumed by border positions so it is enough to specifythe edges ofso we can define an area by determining the border
. The formal definition of this functionfollows:
H Former _ Proximity H New _ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, wehave to save multiple linkeproposed function, we considered one linked list to avoid
complexity.are shown in Fig. 6.Proximity values by determining
waybeing that pombing define
In Fig., which are neighbour
etermine an area for specifying some adjacent positions.mentioned above
positions, but at least three positions should be listedbecause th
irection is updated)
A. Madraky, Z. A. Othman, A. R. Hamdan
International Review on Computers and Software, Vol. 9, N. 3
represent the results of the. The Proximity attribute is defined
specifying membership in a group. This attributedetermines and saveof hair names.their positions, butvicinity radius. The default
Therefore, a hairlaiting function
specificationto the
assumed by border positions so it is enough to specifythe edges of neighbourso we can define an area by determining the border
. The formal definition of this function
H Former _ Proximity H New _ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, wehave to save multiple linkeproposed function, we considered one linked list to avoid
complexity.are shown in Fig. 6.Proximity values by determining
way tobeing that plaiting defineombing define
5, the, which are neighbour
etermine an area for specifying some adjacent positions.mentioned above
positions, but at least three positions should be listedbecause this number of positions is
irection is updated)
A. Madraky, Z. A. Othman, A. R. Hamdan
International Review on Computers and Software, Vol. 9, N. 3
represent the results of the. The Proximity attribute is defined
specifying membership in a group. This attributedetermines and saveof hair names. Data proximities are calculated based ontheir positions, butvicinity radius. The default
Therefore, a hairlaiting function
of the HODMthe neighbour
assumed by border positions so it is enough to specifyneighbour
so we can define an area by determining the border. The formal definition of this function
H Former _ Proximity H New_ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, wehave to save multiple linkeproposed function, we considered one linked list to avoid
complexity.are shown in Fig. 6.Proximity values by determining
to the claiting define
ombing definesthe Proximity values are Buoy3, Buoy4 and
, which are neighbouretermine an area for specifying some adjacent positions.
mentioned abovepositions, but at least three positions should be listed
is number of positions is
irection is updated)
A. Madraky, Z. A. Othman, A. R. Hamdan
International Review on Computers and Software, Vol. 9, N. 3
represent the results of the. The Proximity attribute is defined
specifying membership in a group. This attributedetermines and save
Data proximities are calculated based ontheir positions, but they havevicinity radius. The default
Therefore, a hairlaiting function
of the HODMneighbour
assumed by border positions so it is enough to specifyneighbour
so we can define an area by determining the border. The formal definition of this function
H Former _ Proximity H New_ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, wehave to save multiple linkeproposed function, we considered one linked list to avoid
complexity. Theare shown in Fig. 6.Proximity values by determining
the combinglaiting define
a position as direction.Proximity values are Buoy3, Buoy4 and
, which are neighbouretermine an area for specifying some adjacent positions.
mentioned abovepositions, but at least three positions should be listed
is number of positions is
irection is updated)
International Review on Computers and Software, Vol. 9, N. 3
represent the results of the. The Proximity attribute is defined
specifying membership in a group. This attributes the adjacent position by
Data proximities are calculated based onthey have
vicinity radius. The defaultdoes
laiting functionof the HODM
neighbourassumed by border positions so it is enough to specify
neighbouring area byso we can define an area by determining the border
. The formal definition of this function
H Former _ Proximity H New_ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, wehave to save multiple linkeproposed function, we considered one linked list to avoid
Theare shown in Fig. 6. The pProximity values by determining
ombinglaiting define
a position as direction.Proximity values are Buoy3, Buoy4 and
, which are neighbouretermine an area for specifying some adjacent positions.
mentioned above, we can add or remove thesepositions, but at least three positions should be listed
is number of positions is
International Review on Computers and Software, Vol. 9, N. 3
represent the results of the. The Proximity attribute is defined
specifying membership in a group. This attributethe adjacent position by
Data proximities are calculated based onthey have
vicinity radius. The defaultdoes not
is runof the HODM
neighbour set of a hair if any limited areaassumed by border positions so it is enough to specify
ing area byso we can define an area by determining the border
. The formal definition of this function
H Former _ Proximity H New_ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, wehave to save multiple linkeproposed function, we considered one linked list to avoid
The SQL statements of thThe p
Proximity values by determiningombing
laiting definea position as direction.Proximity values are Buoy3, Buoy4 and
, which are neighbouretermine an area for specifying some adjacent positions.
, we can add or remove thesepositions, but at least three positions should be listed
is number of positions is
International Review on Computers and Software, Vol. 9, N. 3
represent the results of the K. The Proximity attribute is defined
specifying membership in a group. This attributethe adjacent position by
Data proximities are calculated based onthey have different values in terms of
value of this attribute isnot have
is runof the HODM, the middle positions are
set of a hair if any limited areaassumed by border positions so it is enough to specify
ing area byso we can define an area by determining the border
. The formal definition of this function
H Former _ Proximity H New _ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, wehave to save multiple linked lists in Proximity. In theproposed function, we considered one linked list to avoid
SQL statements of thThe plaiting function changes
Proximity values by determiningombing function, with the difference
laiting defines an area as proximity, buta position as direction.Proximity values are Buoy3, Buoy4 and
, which are neighbours of Buoy1. These positionsetermine an area for specifying some adjacent positions.
, we can add or remove thesepositions, but at least three positions should be listed
is number of positions is
International Review on Computers and Software, Vol. 9, N. 3
K-Nearest Neighbourhood. The Proximity attribute is defined
specifying membership in a group. This attributethe adjacent position by
Data proximities are calculated based ondifferent values in terms of
value of this attribute ishave
is run. To reflect, the middle positions are
set of a hair if any limited areaassumed by border positions so it is enough to specify
ing area byso we can define an area by determining the border
. The formal definition of this function
H Former _ Proximity H New _ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, we
d lists in Proximity. In theproposed function, we considered one linked list to avoid
SQL statements of thlaiting function changes
Proximity values by determiningfunction, with the differencean area as proximity, but
a position as direction.Proximity values are Buoy3, Buoy4 and
s of Buoy1. These positionsetermine an area for specifying some adjacent positions.
, we can add or remove thesepositions, but at least three positions should be listed
is number of positions is
International Review on Computers and Software, Vol. 9, N. 3
Nearest Neighbourhood. The Proximity attribute is defined
specifying membership in a group. This attributethe adjacent position by
Data proximities are calculated based ondifferent values in terms of
value of this attribute ishave any
To reflect, the middle positions are
set of a hair if any limited areaassumed by border positions so it is enough to specify
ing area by theso we can define an area by determining the border
. The formal definition of this function
H Former _ Proximity H New _ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, we
d lists in Proximity. In theproposed function, we considered one linked list to avoid
SQL statements of thlaiting function changes
Proximity values by determining the hairfunction, with the differencean area as proximity, but
a position as direction.Proximity values are Buoy3, Buoy4 and
s of Buoy1. These positionsetermine an area for specifying some adjacent positions.
, we can add or remove thesepositions, but at least three positions should be listed
is number of positions is essential for defining
International Review on Computers and Software, Vol. 9, N. 3
Nearest Neighbourhood. The Proximity attribute is defined
specifying membership in a group. This attributethe adjacent position by
Data proximities are calculated based ondifferent values in terms of
value of this attribute isany neighbour
To reflect, the middle positions are
set of a hair if any limited areaassumed by border positions so it is enough to specify
the Proximity attributeso we can define an area by determining the border
. The formal definition of this function
H Former _ Proximity H New _ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, we
d lists in Proximity. In theproposed function, we considered one linked list to avoid
SQL statements of thlaiting function changes
the hairfunction, with the differencean area as proximity, but
a position as direction.Proximity values are Buoy3, Buoy4 and
s of Buoy1. These positionsetermine an area for specifying some adjacent positions.
, we can add or remove thesepositions, but at least three positions should be listed
essential for defining
International Review on Computers and Software, Vol. 9, N. 3
Nearest Neighbourhood. The Proximity attribute is defined
specifying membership in a group. This attributethe adjacent position by
Data proximities are calculated based ondifferent values in terms of
value of this attribute isneighbour
To reflect, the middle positions are
set of a hair if any limited areaassumed by border positions so it is enough to specify
Proximity attributeso we can define an area by determining the border
. The formal definition of this function
H Former _ Proximity H New_ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, we
d lists in Proximity. In theproposed function, we considered one linked list to avoid
SQL statements of thlaiting function changes
the hairfunction, with the differencean area as proximity, but
Proximity values are Buoy3, Buoy4 ands of Buoy1. These positions
etermine an area for specifying some adjacent positions., we can add or remove these
positions, but at least three positions should be listedessential for defining
International Review on Computers and Software, Vol. 9, N. 3
Nearest Neighbourhood. The Proximity attribute is defined
specifying membership in a group. This attributethe adjacent position by using
Data proximities are calculated based ondifferent values in terms of
value of this attribute isneighbour
To reflect the, the middle positions are
set of a hair if any limited areaassumed by border positions so it is enough to specify
Proximity attributeso we can define an area by determining the border
. The formal definition of this function
H Former _ Proximity H New _ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, we
d lists in Proximity. In theproposed function, we considered one linked list to avoid
SQL statements of this functionlaiting function changes
positionfunction, with the differencean area as proximity, but
Proximity values are Buoy3, Buoy4 ands of Buoy1. These positions
etermine an area for specifying some adjacent positions., we can add or remove these
positions, but at least three positions should be listedessential for defining
International Review on Computers and Software, Vol. 9, N. 3
Nearest Neighbourhood. The Proximity attribute is defined
specifying membership in a group. This attributeusing
Data proximities are calculated based ondifferent values in terms of
value of this attribute isneighbour
the, the middle positions are
set of a hair if any limited areaassumed by border positions so it is enough to specify
Proximity attributeso we can define an area by determining the border
. The formal definition of this function
H Former _ Proximity H New_ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, we
d lists in Proximity. In theproposed function, we considered one linked list to avoid
is functionlaiting function changes
positionfunction, with the differencean area as proximity, but
Proximity values are Buoy3, Buoy4 ands of Buoy1. These positions
etermine an area for specifying some adjacent positions., we can add or remove these
positions, but at least three positions should be listedessential for defining
International Review on Computers and Software, Vol. 9, N. 3
Nearest Neighbourhood. The Proximity attribute is defined
specifying membership in a group. This attributeusing a set
Data proximities are calculated based ondifferent values in terms of
value of this attribute is null.beforenatural
, the middle positions areset of a hair if any limited area
assumed by border positions so it is enough to specifyProximity attribute
so we can define an area by determining the borderis
H Former _ Proximity H New_ Proximity
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, we
d lists in Proximity. In theproposed function, we considered one linked list to avoid
is functionlaiting function changes
position in afunction, with the differencean area as proximity, but
Proximity values are Buoy3, Buoy4 ands of Buoy1. These positions
etermine an area for specifying some adjacent positions., we can add or remove these
positions, but at least three positions should be listedessential for defining
International Review on Computers and Software, Vol. 9, N. 3
Nearest Neighbourhoodfor
specifying membership in a group. This attributea set
Data proximities are calculated based ondifferent values in terms of
ull.beforenatural
, the middle positions areset of a hair if any limited area
assumed by border positions so it is enough to specifyProximity attribute
so we can define an area by determining the borderas
(2)
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, we
d lists in Proximity. In theproposed function, we considered one linked list to avoid
is functionthe
in afunction, with the differencean area as proximity, but
Proximity values are Buoy3, Buoy4 ands of Buoy1. These positions
etermine an area for specifying some adjacent positions., we can add or remove these
positions, but at least three positions should be listedessential for defining
International Review on Computers and Software, Vol. 9, N. 3
Nearest Neighbourhoodfor
specifying membership in a group. This attributea set
Data proximities are calculated based ondifferent values in terms of
beforenatural
, the middle positions areset of a hair if any limited area
assumed by border positions so it is enough to specifyProximity attribute
so we can define an area by determining the borderas
(2)
Proximity in formula (2) is defined as a linked listwithout using direction. This attribute is updated bymethods for linked lists. It is able to consider severalplaits if we want to expand this function. In this case, we
d lists in Proximity. In theproposed function, we considered one linked list to avoid
is functionthe
in afunction, with the differencean area as proximity, but
Proximity values are Buoy3, Buoy4 ands of Buoy1. These positions
etermine an area for specifying some adjacent positions., we can add or remove these
positions, but at least three positions should be listedessential for defining
A. Madraky, Z. A. Othman, A. R. Hamdan
Copyright © 2014 Praise Worthy Prize S.r.l. - All rights reserved International Review on Computers and Software, Vol. 9, N. 3
554
Fig. 6. SQL statements for Plaiting method
III.4. Colouring
The colouring function is utilized for grouping non-adjacent positions. It performs as a membership functionand it assigns a number or a string to the Type attribute.
In other words, it determines the colour of a hair,which enables it to define an isochromatic set. Therefore,the members of a cluster are not required to be adjacent.
This function does not change the data structure; it justinitializes an attribute value called Type. This attribute issaved into the table along with the other data and itenables the HODM to achieve a better response timewhen answering related queries. The formal definition ofthe colouring function is as follows:
Colouring :
H Former _Type H New _ Type(3)
This function’s limitation is that each hair can only bea member of one group because the attribute type issimple and can accept only one value. However, it can beexpanded to use multi-value attributes for assigning aposition to several groups or clusters.
The default value of the Type attribute is null, whichdenotes that the hair is not a member of any group.
Colouring is defined in a similar way to the otheranalytic functions but it retrieves and changes the Typeattribute.
The SQL statements are shown in Fig. 7.
Fig. 7. SQL statements for Colouring method
As an example of this function, the Type value isassigned to ‘Cluster 1’ in Fig. 5. This value is allocatedbased on similarities. Diffused or non-adjacent positionscan be related to each other by this function similar to theactivity of colouring hair.
IV. Conclusion and Future Work
In this paper, we first provided some background tothis field by explaining the spatio-temporalcharacteristics of data scale, data type and process type.
We also categorized the analytic functions in this fieldbased on process type and the analytical algorithmsaccording to the literature. We considered three processtypes: spatial, temporal and spatio-temporal. We alsoexplained the three main analytic methods: classification,clustering and association rules. Furthermore, weidentified some important recent articles in this field,grouping them in terms of the proposed algorithms andcontribution to knowledge in this field. We thendiscussed knowledge representation and some of theproblems encountered in spatio-temporal data and thegeneral solution that could be applied for problemsolving.
In the main part of the paper, we focused on the hair-oriented data model (HODM)which can be applied tospatial and temporal information. We introduced this datamodel by describing its structure, specifications and themain reasons for using it. We then defined and explainedthe three analytic functions of the HODM, namelycombing, plaiting and colouring. For each function, westated the applications, formal definitions, advantagesand SQL statements as well as showed how they couldbe applied by using the example of a climate change dataset.
These analytic functions enable better informationrepresentation with more understandability of spatio-temporal data in a model that is inspired by nature,namely, the natural structure of hair. This model cansimplify data analysis and achieve better representationbecause analytic information is embedded with the dataand is updated if the values change. Thus experts andusers can make more effective predictions and moresecure identifications based on such spatio-temporal datadue to the similarity between behaviour and operations.
The most important advantages of this model are thatit can reduce redundancy, which leads to greater dataintegrity and consistency, and that it can separate spatialand temporal data into static data and dynamic types,respectively, while simultaneously preserving therelationship between the two, which leads to thecapability to expand queries and, again, better analyticrepresentation for the benefit of both users and experts.
In addition, data understandability and predictabilityare increased by using the structure and operations thatcan be found in nature. However, the model in its currentform has a limitation in that it cannot accept movableobjects with variable spatial specifications. This problemis inevitable as the model’s features are derived from the
CREATE PROCEDURE HM.PLAITING(PPOSITIONX IN NUMBER,PPOSITIONY IN NUMBER,PPROXIMITY IN HM.POINTSETTYPE
)AS/* TYPE LOCAL VARIABLES DECLARATION HERE */BEGINUPDATE HM.HAIRSET PROXIMITY = PPROXIMITYWHERE POSITIONX=PPOSITIONX AND
POSITIONY=PPOSITIONYRETURN;
END;
CREATE PROCEDURE HM.COLOURING(PPOSITIONX IN NUMBER,PPOSITIONY IN NUMBER,PTYPE IN VARCHAR2 (20)
)AS/* TYPE LOCAL VARIABLES DECLARATION HERE */BEGINUPDATE HM.HAIRSET TYPE = PTYPEWHERE POSITIONX=PPOSITIONX AND
POSITIONY=PPOSITIONYRETURN;
END;/
A. Madraky, Z. A. Othman, A. R. Hamdan
Copyright © 2014 Praise Worthy Prize S.r.l. - All rights reserved International Review on Computers and Software, Vol. 9, N. 3
555
characteristics of natural hair.In the future, we aim to define and implement the
security functions of the HODM. These data protectionfunctions in the HODM are called Covering, Tanglingand Wig. In addition, the performance of themaintenance, analysis and security functions of theHODM need to be evaluated by comparing them withthose of other models based on standard metrics.
It is also essential to further develop the model forreal-world application by preparing soft tools withgraphic user interfaces. The preparation of a modelapplication for use in different environments such as anurban transit system, power resources management andeducational information systems is expected to lead toimprovements in the structural and analytical definitionspresented thus far.
Acknowledgements
This work is supported by the Fundamental ResearchGrant Scheme (Project no. FRGS/1/2012/SG05/UKM/02/6) under the Excellence Planning Division,Department of Higher Education of Malaysia.
The authors appreciate the constructive comments ofthe anonymous reviewers, which has contributed to theimprovement of this paper.
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Authors’ informationData Mining and Optimization Research Group (DMO), Centre forArtificial Intelligence Technology (CAIT), School of ComputerScience, Faculty of Information Science and Technology, UniversitiKebangsaan Malaysia (UKM), Malaysia.
Abbas Madraky received a BE degree inSoftware Engineering in 1990 from TehranUniversity and a MSc degree in ComputerEngineering in 2001 from Azad University,Iran. He is currently a PhD candidate inComputer Science at UKM. His researchinterests include data warehousing, data miningand knowledge discovery especially in spatial
and temporal systems. Mr Madraky is a student member of the IEEE.E-mail: [email protected]
Zulaiha Ali Othman was born in Malaysia.She was awarded a PhD in Computing bySheffield Hallam University, England, in 2004.Since then she has been a lecturer andresearcher in the DMO, Faculty of InformationScience and Technology, UKM. Her researchinterest lies in applying and improving datamining, focusing on spatio-temporal data
mining and related areas. Associate Prof Dr Zulaiha is a member ofseveral computer science committees. She has written more than 100conference and journal papers that have been published around theworld and has several research awards.E-mail: [email protected]
Abdul Razak Hamdan is Malaysian and wasawarded his PhD by Loughborough University,England. Currently, he is a Head of the DataMining and Optimizations Research Group,Faculty of Information Science and Technology,UKM. He is well known as the father ofArtificial Intelligence in Malaysia as he hasinvented and been involved in many technical
developments and the application of AI research in various domains.He has a large number of high-quality publications and awards to hisname.E-mail: [email protected]