Download - 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.


Copyright © 2014 Praise Worthy Prize S.r.l. - All rights reserved International Review on Computers and Software, Vol. 9, N. 3

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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.



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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]



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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.

Copyright © 2014 Praise Worthy Prize S.r.l.

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


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


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


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


Day123456789

10111213123456789

10111213


(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


Day1234567891011121312345678910111213


temperature. Datar some locations.

the HODM isa data


coordinates inare repeated in all of the rows



data transfer costs in large databases. In other wordstatic part


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HODM isdata


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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


HODM isset

integrity and consistencyThe location number is

Latitude and Longitude columnsare repeated in all of the rows


X and Positionto decrease database size, response time and



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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



data transfer costs in large databases. In other wordstatic part of the data)


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

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Latitude and Longitude columnsare repeated in all of the rows for



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in HODM structure, whichlocation specifications are saved


data transfer costs in large databases. In other wordof the data)


- 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


(mer.)north>0), relative humidity, air temperature and sea



integrity and consistency isthe

Latitude and Longitude columnseach position, whichare saved


data transfer costs in large databases. In other wordof the data) is

Fig.


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


winds (south<0,north>0), relative humidity, air temperature and sea



increased.Buoy column and

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Y attributes.to decrease database size, response time and

data transfer costs in large databases. In other wordis saved in the

Fig. 2.


All rights reserved

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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.)





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is illustrated inare saved just one time

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All rights reserved

L NINO

of the following variables: buoy(zon.)


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that it is able to reduceby removing repeated spatial


Latitude and Longitude columnseach position.

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data transfer costs in large databases. In other wordsaved in the

El Nino Sub


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INO SZon.Winds

of the following variables: buoy(zon.) winds


from the44], one

to reduceby removing repeated spatial


Latitude and Longitude columns



data transfer costs in large databases. In other wordssaved in the

El Nino Sub


TABLEUBD

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of the following variables: buoywinds


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Thisto decrease database size, response time and

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551

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International Review on Computers and Software, Vol. 9, N. 3

RIGINAL FORM

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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


but temporal data, which canof the hair and


mixtureing


ues is shown in Fig.the value of n


S.S.Temp.27.5727.6227.6827.7

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dynamic part

to representstructural


which canand is grown


ture of dataStrength,

ype. In the implementationnested table to define

ues is shown in Fig.the value of null.


S.S.Temp.

dynamic part of the



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.


of the



which canis grown

n theair is defined by a record that has some

of datatrength,


ues is shown in Fig.


of the



which canis grown

n theair is defined by a record that has some

of datatrength,


ues is shown in Fig.


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


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


The initial value ofStrength value is consideredimportanceinserted the


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.


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


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.




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


initial value ofStrength value is considered

for instancedata values of two positions as an instance.


should bedefined in two rows of


Definitions of the

As mentioned above, tthree analytical functions

ing. These functions are usedclassification or clustering methodsdefined according to






string form as a fuzzy value baseused for reporting, quer

uncertain data accordingdition, by using this






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


Definitions of the


ing. These functions are usedclassification or clustering methodsdefined according to




attributesProximity and Type.



accordingby using this






ositionbased on the structure through the save spatial data in arow and temporal data in a nested tablefinal schema is shown in Fig.



Definitions of the


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.



accordingby using this

significant positions are identified in order tothem. The other analytic attributesProximity and Type, which in the HODM are



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.



Definitions of the Proposed


ing. These functions are usedclassification or clustering methods

the natural specification of hair.These functions do not

data mining algorithmsHODM


The new attributes allow us todefinition and decrease reprocess analytical findings by changing or recording

attributes, which are



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


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


Proposed

As mentioned above, this paper focuses on definingthree analytical functions


natural specification of hair.involve the proposition of

data mining algorithms, butHODM, we propose a new way


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


to data reliability or probability.by using this

significant positions are identified in order tothem. The other analytic attributes


explained in following subsections


Direction is equal to Position and‘necessary


(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


natural specification of hair.involve the proposition of, but, we propose a new way


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


subsections


Direction is equal to Position andecessary


isbased on the structure through the save spatial data in arow and temporal data in a nested table

emphasized that thethe hair


Proposed A

his paper focuses on definingcalled

ing. These functions are usedclassification or clustering methods,


rather use available, we propose a new way


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


subsections



Fig. 3. El Nino Subdata in HODM for two positions

Direction is equal to Position andecessary


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


Analytical

his paper focuses on definingcalled Combing, Plaiting

ing. These functions are usedand their features are




allow us to expand the querysponse time. It is possible to

process analytical findings by changing or recording, which are Strength, Direction,


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


subsections.


All rights reserved


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


nalytical

his paper focuses on definingCombing, Plaiting

ing. These functions are usedand their features are




expand the querysponse time. It is possible to

process analytical findings by changing or recordingStrength, Direction,


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


All rights reserved


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


nalytical


ing. These functions are used to representand their features are






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


All rights reserved



Tabledata values of two positions as an instance.

defined in thebased on the structure through the save spatial data in a

called Cell


nalytical Functions


to representand their features are







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


All rights reserved



Table IIdata values of two positions as an instance.

defined in the modelbased on the structure through the save spatial data in a

Cell.


unctions











the nonremove

Direction,dealt withns, which




II, wedata values of two positions as an instance.

modelbased on the structure through the save spatial data in a

The


unctions



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 nonremove



552



, we

modelbased on the structure through the save spatial data in a

The


unctions



newrather use available

todata modelling and representation based on new






the non-remove



552


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



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



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 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






to representby a position’sof a related data set is stored in an attribute

as has noted inattribute is initialized by


define data similarity or relationships in fuzzy formthe direction

a specific radius. The formal definition of this functionas follows:


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





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


(1),called ‘Hair


, but it is better to select adjacent objects formore similarity with

assignthis function so we can use a set of hair for input or


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.





to represent objects in a class and it is determinedcoordinates.




directiona specific radius. The formal definition of this function


(1), HHair


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


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


III.2.


objects in a class and it is determinedcoordinates.




direction could be considereda specific radius. The formal definition of this function

Combing :


is defined as a set of related data to aHair’ in


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



coordinates of a point are used forDirection value and it could

be different from data positions. In, Direction values can



III.2.


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


could be considereda specific radius. The formal definition of this function

Combing :


is defined as a set of related data to ain the


but it is better to select adjacent objects forthe natural model.





coordinates of a point are used forcould




Combing


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



Combing :


is defined as a set of related data to athe HODM and Direction is used


but it is better to select adjacent objects fornatural model.





coordinates of a point are used forcould determine a




Combing


objects in a class and it is determinedIn the

of a related data set is stored in an attribute]. Direction as an analyticombing function.


define data similarity or relationships in fuzzy formcould be considered

a specific radius. The formal definition of this function

Combing :


is defined as a set of related data to aHODM and Direction is used




output. The default value for thePosition attribute. In this case, the



coordinates of a point are used fordetermine a


position which we have not any data related to iteffects of the combing function


Combing


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



Combing :






the Direction attPosition attribute. In this case, the



coordinates of a point are used fordetermine a


position which we have not any data related to itombing function



objects in a class and it is determinedHODM,


ombing function.We assign some data to a class with








Direction attPosition attribute. In this case, the


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



objects in a class and it is determinedHODM,


ombing function.with


could be considered as a zone witha specific radius. The formal definition of this function




but it is better to select adjacent objects for




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



objects in a class and it is determinedHODM, the


ombing function.the same or


as a zone witha specific radius. The formal definition of this function





the common direction to a set of hairthis function so we can use a set of hair for input or





n area of hair.be different from data positions. In

be pointed toposition which we have not any data related to it

ombing function



objects in a class and it is determinedthe location


the same ordirections. As can be deduced, it is possible to

define data similarity or relationships in fuzzy formas a zone with






a set of hairthis function so we can use a set of hair for input or

Direction attribute isPosition attribute. In this case, the




of hair.be different from data positions. In

be pointed toposition which we have not any data related to it.

ombing function



objects in a class and it is determinedlocation

called. Direction as an analytic






toon



ribute isPosition attribute. In this case, the




of hair.be different from data positions. In

be pointed toFig. 5

on


This function sets the data direction or orientation fora set of data about a position. Direction or orientation is





a specific radius. The formal definition of this function is

(1)


thisthe




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


This function sets the data direction or orientation foris





is

(1)


thisthe




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


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


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;/


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


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


END;


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


Plaiting is utilized as another analytic function infor clustering representation.

function assigns a set of adjacent hIt is


/* TYPE LOCAL VARIABLES DECLARATION HERE */

UPDATE HM.HAIRSET DIRECTION=PDIRECTIONWHERE POSITIONX=PPOSITIONX AND




howto Buoy2 so that the two positions are assigned tosame class. Note that

thefunction is used for output representation


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







how Direction changto Buoy2 so that the two positions are assigned tosame class. Note that

the relevant algorithms andfunction is used for output representation


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






POSITIONY=PPOSITIONY



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.









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



III.3.


function assigns a set of adjacent halso used as a classification method to



SET DIRECTION=PDIRECTIONWHERE POSITIONX=PPOSITIONX AND







data values in thDirection attribute of Buoy1



Therefore, the Direction valueDirection value is assigned in



Plaiting


function assigns a set of adjacent has a classification method to






Fig. 4. SQL statements for C




data values in thDirection attribute of Buoy1 is



valueDirection value is assigned in



Plaiting


function assigns a set of adjacent has a classification method to





Combing

Direction changesto Buoy2 so that the two positions are assigned to



data values in this equal to


other analytic attributesthese changes are

value may be different fromDirection value is assigned in



Plaiting


function assigns a set of adjacent hairs or positions into aas a classification method to


PDIRECTION IN HM.POINTTYPE





ombing

from position Buoy1to Buoy2 so that the two positions are assigned to

as mentioned aboverelevant algorithms and


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


Plaiting


airs or positions into aas a classification method to

Fig.


PDIRECTION IN HM.POINTTYPE


WHERE POSITIONX=PPOSITIONX AND


All rights reserved

ombing method


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


data grouping based on specified conditions.longitude




Fig. 5.


WHERE POSITIONX=PPOSITIONX AND


All rights reserved

ethod


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




is utilized as another analytic function inThe p


El Nino Sub



All rights reserved

ethod


, classificationthe cthe Direction

but this attribute is applied for performingexample,the

default value. It is possible to changethe c

other analytic attributesexplained




is utilized as another analytic function inThe p


El Nino Sub



All rights reserved


, classificationthe combing

Directionbut this attribute is applied for performing

example,position

default value. It is possible to changethe combing

other analytic attributesed in


data grouping based on specified conditions.of more


is utilized as another analytic function inThe plaiting


El Nino Sub



from position Buoy1to Buoy2 so that the two positions are assigned to the

, classificationombing


example, theposition

default value. It is possible to changeombing

other analytic attributesin the


data grouping based on specified conditions.more


is utilized as another analytic function in thelaiting


El Nino Subdata in HODM (



553

from position Buoy1the

, classificationombing


theposition

default value. It is possible to changeombing

other analytic attributesthe


morethan 8 are directed to the specific position for data

thelaiting


ata in HODM (


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 (



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



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)



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


positions, but at least three positions should be listedbecause th

irection is updated)



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


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




way tobeing that plaiting defineombing define

5, the, which are neighbour


positions, but at least three positions should be listedbecause this number of positions is





specifying membership in a group. This attributedetermines and saveof hair names. Data proximities are calculated based ontheir positions, butvicinity radius. The default


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




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





specifying membership in a group. This attributedetermines and save

Data proximities are calculated based ontheir positions, but they havevicinity radius. The default


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



complexity. Theare shown in Fig. 6.Proximity values by determining

the combinglaiting define

a position as direction.Proximity values are Buoy3, Buoy4 and


mentioned abovepositions, but at least three positions should be listed





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




Theare shown in Fig. 6. The pProximity values by determining

ombinglaiting define



mentioned above, we can add or remove thesepositions, but at least three positions should be listed




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 SQL statements of thThe p

Proximity values by determiningombing

laiting definea position as direction.Proximity values are Buoy3, Buoy4 and


, we can add or remove thesepositions, but at least three positions should be listed



represent the results of the K. The Proximity attribute is defined


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




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.




K-Nearest Neighbourhood. The Proximity attribute is defined


Data proximities are calculated based ondifferent values in terms of

value of this attribute ishave

is run. To reflect, the middle positions are





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


s of Buoy1. These positionsetermine an area for specifying some adjacent positions.




Nearest Neighbourhood. The Proximity attribute is defined



value of this attribute ishave any

To reflect, the middle positions are


ing area by theso we can define an area by determining the border






Proximity values by determining the hairfunction, with the differencean area as proximity, but




is number of positions is essential for defining





value of this attribute isany neighbour



the Proximity attributeso we can define an area by determining the border






the hairfunction, with the differencean area as proximity, but




essential for defining





value of this attribute isneighbour



Proximity attributeso we can define an area by determining the border






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



specifying membership in a group. This attributethe adjacent position by using



To reflect the, the middle positions are







SQL statements of this functionlaiting function changes

positionfunction, with the differencean area as proximity, but






specifying membership in a group. This attributeusing



the, the middle positions are







is functionlaiting function changes

positionfunction, with the differencean area as proximity, but






specifying membership in a group. This attributeusing a set


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




is functionlaiting function changes

position in afunction, with the differencean area as proximity, but





Nearest Neighbourhoodfor

specifying membership in a group. This attributea set


ull.beforenatural



so we can define an area by determining the borderas

(2)



is functionthe

in afunction, with the differencean area as proximity, but





Nearest Neighbourhoodfor

specifying membership in a group. This attributea set


beforenatural



so we can define an area by determining the borderas

(2)



is functionthe

in afunction, with the differencean area as proximity, but






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


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


END;/



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]