International Review on Computers and Software

International Review on Computers and Software

(IRECOS)

Contents: Location Aided Cluster Based Geographical Routing Protocol for Intermittently Connected MANET by B. Muthusenthil, S. Murugavalli

1

Eliminating False Data and Improving Network Lifetime Using Mobile Data Collector in Wireless Sensor Networks by Sandhya M. K., Murugan K.

9

Non Cooperative Power Control Game with New Pricing for Wireless Ad Hoc Networks by Sanjay Kumar Suman, Dhananjay Kumar, L. Bhagyalakshmi

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Meta-Analysis of Ontology Software Development Process by Marzanah A. Jabar, Mustafa S. Khalefa, Rusli Hj. Abdullah, Salfarina Abdullah

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Cluster Based AODV Protocol to Minimizing the Route Rediscovery Process and to Improve the Lifetime of Network in MANET by R. Senthil Kumar, C. Sureshkumar

38

An Efficient Autonomous Key Management with Verifiable Secret Sharing Schemes for Reduced Communication/Computation Costs in MANET by M. Devi, S. Chenthur Pandian

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An Optimized Inference of Pattern Recognition Using Fuzzy Ant Based Clustering Algorithm by K. Sathesh Kumar, M. Hemalatha

54

Componentized Service Oriented Architectural Model for Power System Problems by V. Gomathi, V. Ramachandran

64

Design and Modeling of SMA Architecture Using MaSE Methodologies by R. Moussaoui, H. Medroumi, F. Moutaouakkil

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Improved Parallel Pattern Growth Data Mining Algorithm by P. Asha, T. Jebarajan

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Innovative Features in Pathological Tissues Segmentation and Classification of MRI Brain Images with Aid of Back Propagation Neural Network by J. C. Smitha, S. Suresh Babu

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Services and Agents Based Mediation System Architecture (SAMED Architecture) by T. Rachad, S. Elghazi, J. Boutahar

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(continued on inside back cover)

ISSN 1828-6003Vol. 9 N. 1

January 2014

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Copyright © 2014 Praise Worthy Prize S.r.l. - All rights reserved

International Review on Computers and Software (IRECOS)

Editor-in-Chief:

Prof. Marios Angelides Brunel University School of Engineering and Design Electronic and Computer Engineering Department Uxbridge - UB8 3PH U.K.

Editorial Board:

Mikio Aoyama (Japan) Pascal Lorenz (France) Francoise Balmas (France) Marlin H. Mickle (U.S.A.) Vijay Bhatkar (India) Ali Movaghar (Iran) Arndt Bode (Germany) Dimitris Nikolos (Greece) Rajkumar Buyya (Australia) Mohamed Ould-Khaoua (U.K.) Wojciech Cellary (Poland) Witold Pedrycz (Canada) Bernard Courtois (France) Dana Petcu (Romania) Andre Ponce de Carvalho (Brazil) Erich Schikuta (Austria) David Dagan Feng (Australia) Arun K. Somani (U.S.A.) Peng Gong (U.S.A.) Miroslav Švéda (Czech) Defa Hu (China) Daniel Thalmann (Switzerland) Michael N. Huhns (U.S.A.) Luis Javier García Villalba (Spain) Ismail Khalil (Austria) Brijesh Verma (Australia) Catalina M. Lladó (Spain) Lipo Wang (Singapore)

The International Review on Computers and Software (IRECOS) is a publication of the Praise Worthy Prize S.r.l.. The Review is published monthly, appearing on the last day of every month. Published and Printed in Italy by Praise Worthy Prize S.r.l., Naples, January 31, 2014. Copyright © 2014 Praise Worthy Prize S.r.l. - All rights reserved. This journal and the individual contributions contained in it are protected under copyright by Praise Worthy Prize S.r.l. and the following terms and conditions apply to their use: Single photocopies of single articles may be made for personal use as allowed by national copyright laws. Permission of the Publisher and payment of a fee is required for all other photocopying, including multiple or systematic copying, copying for advertising or promotional purposes, resale and all forms of document delivery. Permission may be sought directly from Praise Worthy Prize S.r.l. at the e-mail address: [email protected] Permission of the Publisher is required to store or use electronically any material contained in this journal, including any article or part of an article. Except as outlined above, no part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior written permission of the Publisher. E-mail address permission request: [email protected] Responsibility for the contents rests upon the authors and not upon the Praise Worthy Prize S.r.l.. Statement and opinions expressed in the articles and communications are those of the individual contributors and not the statements and opinions of Praise Worthy Prize S.r.l.. Praise Worthy Prize S.r.l. assumes no responsibility or liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained herein. Praise Worthy Prize S.r.l. expressly disclaims any implied warranties of merchantability or fitness for a particular purpose. If expert assistance is required, the service of a competent professional person should be sought.

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Manuscript received and revised December 2013, accepted January 2014 Copyright © 2014 Praise Worthy Prize S.r.l. - All rights reserved

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Meta-Analysis of Ontology Software Development Process

Marzanah A. Jabar, Mustafa S. Khalefa, Rusli Hj. Abdullah, Salfarina Abdullah Abstract – Ontology is an important concept in Computer Science to formally represent knowledge. The software engineering ontology assists in defining information for the exchange of semantic project information framework. Research into ontological issues has been widely active in various areas. This paper presents the origin of ontology research and gives the different definitions of ontology. The paper gives an overview of ontology and its types including the building and design for an enterprise system.This review tries to study articles in which adaption model and there properties were discussed in order to get a clear review about the ontology. This study is showed a Systematic Literature Review which was used to identify important characteristic about the ontology. The research identified more than 70 paper on this study but only 46 of them was precisely relevant in the field of Ontology development processs. In this paper we analyse the ontology based on types, design, building ,model as a systematic review of the subject. And choose the best way according to that. Copyright © 2014 Praise Worthy Prize S.r.l. - All rights reserved. Keywords: Ontology, Types of Ontology, Building, Design of Ontology

I. Introduction Linda Anticoli, Elio Toppano and Pisanelli D. M.,

Gangemi A. ,Steve G. [1], [2] defined ontology as a specific and~ a clear picture of an abstract world to be presented in a clear simplified view for some purpose whereby vocabularies are extracted from a domain.

Having a clear picture may lead to the concepts and their relations to be extracted from the real world [3].

Since ontologies use similar shared vocabularies when describing ideas and relations they can also be used as tools in specifying the meaning of terminology systems in a clear defined manner. The authors in [4] identified three major significant uses of ontologies which are, (i) to help in communication between human beings, (ii) to make communication among software systems possible, and (iii) to improve the Enterprise system (ES) design and quality of software development process.

The work in [5] added that a new temporal ontology for a virtual domain that will contain all related domains should be designed. The new domain should be designed using the following procedures as identified by [6] to identify the purpose, to capture, perform the coding, integrate with any existing ontology, perform evaluation and complete the documentation of the ontology. in [7].

There is a convention in the ontology group that the incorporation of current ontologies is a more valuable way to make a new ontology and thus cut down on the time, price, and attempts required [1]. Ensuring semantic interoperability among different ontologies for incorporation is a key aspect for building ontologies effectively and guaranteeing semantic interoperability across the domain [8].

An ontology of a given domain explains the constituents of truth within that domain in a methodical way, as well as the interaction between these constituents and their kindred to constituents of other domains. Conditions such as 'domain', 'constituent', 'reality', and 'relation' are themselves ontological terms, as also are terms such as 'feature', 'object', 'entity', and 'item', as well as 'being' and 'existence' themselves.

II. Related Work The researchers in [9] addressed that the engineering

domain analysis activities are distributed across processes. Furthermore, they tested the Ontology Based Domain Engineering (ODE) for getting a better result in term of software quality by applying the ODE in different domain for varies practical application.

The authors in this paper, proposed an ontology-based approach to domain engineering that considers two main phases: building ontologies and deriving object frameworks from them. However, the technique does not provide explicit concept, which it is a common problem in engineering domain. Another mechanism could be included to the ODE which has been used by them.

In [10] identified the aspects of user modeling relevant to Knowledge Management System (KMS) and integrated them in a generic framework based on ontologies.

They showed a good example of the use of the Ontology Based user Modeling (OntobUM) framework in an ontology-based KMS. On the other hand, this framework can be adapted to different ontology-aware environments, therefore, the user ontology is

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implemented using semantic web technology and it is structured on extended Information Management Systems Learner Information Package Specifications (IMS LIP). Besides that, the most significant part of the user ontology is common and it could be reused in another application domains.

The drawback of the presented work is a slow search process when large database applied for KMS. In [11] raised and discusses about how people cannot share their knowledge when they do not speak a common language. The authors presented an ontology model of software engineering to solve this problem by using special ontology characteristic of the software engineering ontology increment to give analysis for explaining the software engineering, consist and its usage. Moreover, they made a new model in graphical notion unified modeling language (UML) to share the knowledge which has been implemented in Ian sommervil book. However, the paper presented can have many future works .a new case study for different application and can implement in another book or another edition this is can be new introduction concept. The efficiency of the implemented approach should be included for determining the model performance. In [12], touched the problem of how to communicate with team from different site location, and how to solve this problem by latest technology using ontology shared system to distributed fashion for developed software. This research presented the ontology for a given software project that well developed using the rudimentary software principle. Then it focuses on an ontology model of software engineering to represent this knowledge.

The development of their project as windows application was tested by using visual studio framework with C# program language and they used a practical case study for their implementation. Nowever , the paper did not cover all aspect of software engineering such as testing and quality and new approaches affecting every single aspect in software engineering. Which may need future work for farther development.

In [14] Mohamed Nazir using knowledge management for enterprise system because the companies face the challenges for expanding their markets and no framework appear available to understand this integration in ontology. The researcher in this research examine the role of ontology based KM for ES to investigates the possible integration of ontology.

As well as the researcher try to develop the taxonomy as framework for understanding Ontology Based Knowledge Management for Enterprise system (OKES) research and to systematic review of existing research conducted.

This paper select new business process, data analysis and conversion, cross the complete ES lifecycle.

The result of this work it consist of three steps planning the review, conducting the review, reporting the dissemination. The motivate in this paper about OKES research, this work in the future need to investigate in depth for empirical study of this research.

In [13] Ian Sommerville research about encountered in the multi –site environment is all about remote communication and what the text on also transcription is difficult because work is carried out in an environment where development teams are geographically distributed and team member are involved in many project.

The research was to present understanding of software engineering project information to all the distribution member of development team in multi-site development environment, to present the benefit in a distributed multi-site development environment.

The research is implementing using UML class diagram that will be transformed (transforming process) into the class diagram ontology model as instance knowledge, to explored software engineering knowledge formed in the software engineering ontology, we have to analysed the instantiations in the software engineering through the example. in this research can make modify to the future work by implement this program or design with another data to have new result.

III. Method At the first level of abstraction there are roughly 260

papers founded using the key words about ontology, design ontology, building ontology, classification, advantage of ontology, ontology. language, KM, enterprise system development life cycle, software development process, knowledge flow.

After refinement to the year between 2002 and 2013 only 245 papers were appeared. 73 primary article also known as anchor paper used in identifying the knowledge area in ontology history, design, type, Km and enterprise software development process. 46 papers from 2003 till 2013 were identified that related most areas.

The results of the critical analysis are tabulated as gap analysis.Gap analysis are based on problem statement, objective, finding, solution, methodology used and result or future work suggestion. Problem statement will be derived from the gap analysis of related work. Reaserch question and objective will be formulated for the proposal research.

IV. Discussion Although ontology is a well-known method to resolve

mutual understanding among heterogeneous system. The author in [15], identified three major of ontology significant uses of ontology which are , (i) to help in communication between human being, (ii) to make communication among software system possible and (iii) to improve enterprise system. In [36] add that new temporal ontology for virtual domain that will contain all related domains should be designed.

The new domain should design following procedures as identified in [6] to identify the purpose, to capture , perform the coding integration with any existing ontology. Furthermore , several ontology from distinct advantage factors therefore it is not surprising that

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different categories of ontologies are discovered with different concentrates. According to the generality stage [15] of the opinion that ontology types are available in four point (high-level ontology , domain ontology , task ontology application ontology ). On the other hand , in [45] recognized the the substitute classification on ontology in four part ( Generic or common – sense ontology , representational ontologies , domain ontology , method ontology).

During the development of new ontology, style concept are needed to foster growth and provide a basis for assessment [54] recognize five style principles which is design and enhancement ontologies (Clarity, Coherence, Extendibility, Minimum encoding bias, Minimal ontological) in addition there is three stage for building an ontology enterprise are recommended by [57] In this paper we have describe definition of ontology as having a clear picture of may lead to the concept and their relation to be extracted from the real world. These papers propose the general level of ontology types and ontology classification and how to build ontology with main component for ontology design with a brief overview about enterprise software development process and ontology language.

In depth the table above describe the all the point with the component. In [14] Highly support HLI, DO in type of ontology and un support TO,AO of ontology type. In classification ontology he highly support GCO,RO,DO and partially support MTO. Partially support the new design with very highly support building ontology. In [34] highly support the design of ontology to build new model with partially support to the type of ontology and un support for the other. In [32],[26] the authors un support the type of ontology except [18] highly support the HLI,DO in type. In addition the authors support the classification highly and partially. They support the design highly and partially just the [32] un support for the design. They are not support the building of ontology just partially. In [20],[24],[23],[33], the references un support the whole part of type of ontology just they support DO highly.

In classification the highly support all type of classification except the DO,MTO the supported partially and or not support some time. In design of ontology they supported partially. In building ontology the references supported partially. In [17] the author highly support the HLI,DO from type of ontology and partially support the others type (TO,AO).

The author support all classification type highly. In design the author supported all part of design highly and un support building ontology. In [19],[30],[26] the authors un supported the all part of type of ontology just [19] support the HLI, DO from the type of ontology. The authors highly and partially support for whole type of classification ontology. In design and building the references highly and partially support for whole part of them. In [31] the author un support all point in the Table I above just spatially. Support all classification point in table1. In [16],[25],[35] the authors un support all point

of type ontology just the first two HLI, DO highly support. They highly and partially support classification ontology in addition the authors support highly and partially the design of ontology, partially support for the building ontology. In [21],[42],[43] author support DO, TO, HA highly in type of ontology and un support to the HLI, on the other hand the author un support for classification ontology. [21] support highly design part ontology and building of ontology. In [45],[48] references they are un support for type and classification ontology in whole part of them, but they support partially design and building ontology. In [28] the author un support for the type of ontology and classification of ontology and design just highly support the building of ontology.

In summary what the authors suggest , implement , design and which one the best one and more close with our work. This research present the ontology view with ontology structure also ontology types (High level ontology, Domain Ontology, Task Ontology, Application Ontology), Classification Ontology which include (Generic Common-Sense, Representational Ontology ,Domain Ontology, Method and Task Ontology), Ontology Design (Clarity, Coherence, Extendibility, Minimum Encoding Bias, Minimal Ontology Commitment) In addition Buliding Ontology (Capture, Coding, Integration with Existing Ontology ). Mohamed Nazir 2013 it was the best one in proposed above because his research include all four points (Type, Classification, Design, Building) with implementation and easy way in addition the author make new model design for building ontology. Moreover , they give two advantages for ontology based knowledge management. First we can benefit from promising steps towards the purpose and goals given by the ontological approach. Second knowledge management process as mechanisms for managing enterprise system development related knowledge are well guided by systematic and concrete method tools support by ontology. As well as supporting knowledge management process through ontology for enterprise system can present more than one fruitful benefit.

V. Benefit the Use of Ontology Ontology's prepare some profit in the process of

developing software, that can be summarized based on [38], [39], [40], [41], [42], [43].

Ontology's prepare to represent vocabulary specifically to software processes, eliminating conceptual and terminological mismatches, without existing model ontology technique solve the problem ontology can help to enhance the benchmark of processing software with data collection in internet, and using the semantic web. An ontology can help to transfer the knowledge and at the same time to simplify developing the cycle from one project to project ontology's facilitate the processes of knowledge acquisition by sharing same conceptualization for different software applications, by sharing the

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understanding also communication between users during the ontological analysis terminological and conceptual mismatches decrease. Ontology's as a machine

understandable representation helps software angering activities such as developing the tools.

TABLE I

ANALYSIS OF ONTOLOGY FRAMEWORK FOR ENTERPRISE SOFTWARE DEVELOPMENT

: highly supported : partially supported : unsupported Type: HLO: (High-level ontology), DO : (Domain ontology), TO : (Task ontology), AO : (Application ontology) Classification: GCO: (Generic common-sense), RO : (Representational ontology), DO : (Domain ontology), MTO: (Method and task ontology) Design: CL: (Clarity), CO: (coherence), EX: (extendibility), MEB: (Minimum encoding Bias), MOC: (Minimal ontology commitment) Building: CA : (capture), CO : (coding), IEO : (Integration with existing ontology )

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The initial activities which performed in all engineering activity is to purpose system and the intended use of it ,the engineering of ontology start with a proper questions and scenarios of use [14],by asking the proper question we can determine the scope of ontology, and also good question can help in evaluating the ontology.

VI. Ontology Types General level of ontological types

Furthermore, several ontologies form distinct advantage factors Therefore it is not surprising that different categories of ontologies are discovered with different concentrates. According to the generality stage, Guarino is of the opinion that the following ontology types are available [44].

High-level ontologies: Explain common ideas such as space, time, materials and items. They are separate from a particular domain or problem. Their objective is to unify requirements between large groups of users. For example, Gene Ontology (GO).

Domain ontologies: These explain the language relevant to a common domain (for example, computer or medicine), through the expertise of the presented ideas of high-level ontologies. For example, Ontology Design OntoDesign.

Task ontologies: Are used to explain the language relevant to a common task or action (for example, development or sales), through the expertise of the presented ideas of high-level ontologies. For example, Task Model Ontology (TOM).

Application ontologies: Explain ideas that belong at the same time to a domain and a process, by means of the expertise of the ideas of domain ontologies and task ontologies. They are generally matched to tasks performed by the sector organizations when performing an activity. For example, Experimental Factor Ontology (EFO) is used to represent a sample variable from gene expression experimental data. On the other hand, Fensel [45] recognized the following substitute classification:

Generic or common-sense ontologies: Catch common information of the world. They provide primary thoughts and ideas for area, time, state, activities, etc., and are valid for a wide range of domains. For example, Generic Ontology Matching and Mapping Mangament (GOMMO).

Representational ontologies: These are not supposed to be attached to any particular domain. They provide entities without developing what they might signify. Hence, they determine notions which show information in an object or framework focused approach. For example, Ontology Based Knowledge Represenation for bioinformatics (OBKRB).

Domain ontologies: Catch the information legitimate for a particular kind of domain (for example, Basic Formal Ontology (BFO).

Method and task ontologies: The former provide terminologies particular to problem resolution methods,

while the latter provide conditions for particular projects. Both provide an affordable perspective as to the understanding of the domain. For example Logic Based Methods for Ontology Mapping (LOG MAP). Using the type of conceptual framework, Van Heijst and colleagues recognized the following types of ontologies [46]:

Terminological ontologies: Specify expressions to be used to signify the information of an examined domain. Then try to acquire a specific language relevant to a specified area. An example of this kind would be the ULMS (Universal Medical Language System). ONIONS (Ontological Integration Of Naive Sources).

Information ontologies: Specify the framework of data archive information, identifying a framework for the consistent storage of information. An example is the framework for modelling individual medical patient history. For example, Information Artifact Ontology (IAO).

Knowledge representation ontologies: Specify knowledge conceptualizations with an internal structure that exceeds those of the previous ones. They tend to be focused on a description of a particular knowledge use. For example, the Systematised Nomenclature of Medicine Clinical Terms (SNOMED CT) is a clinical ontology.

Another possible way of identifying ontologies is according to the characteristics of the real-world problem that is to be exhibited. In this way, Jurisica et al. have acknowledged the following classes [47], [48] , [49]:

Static ontologies: Explain features that are available, their characteristics and the connections current between them. This category supposes that the globe is made up of entities which are blessed with an exclusive and unchangeable identification. In these, we use conditions such as entity, attribute, or relationship. For example, Biomedical Resource Ontology (BRO).

Dynamic ontologies: Explain the factors of the patterned world which can be modified over time. To design these it may be necessary to use limited state machines, Petri nets, etc. 'Process', 'condition', or 'stat conversions' are examples of Ontology Based Data Access (OBDA).

Intentional ontologies: Explain the factors of the world of inspirations, objectives, values, choices and elections of the engaged providers. Some model terms in these types of ontologies are 'element', 'object', 'broker', or 'support'. For example, Phenotypic Quality Ontology (PATO).

Social ontologies: Explain social factors such as organizational components, netting or interdependences. For this reason they include terms such as 'actor', 'position', 'part', 'authority', 'responsibility' or 'commitment'. For example , Ontology for Parasite Lifecycle (OPL).

VII. Ontology Design During the development of a new ontology, style

concepts are needed to foster growth and provide a basis

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for assessment. Reference [50] recognizes five style principles which should guide the enhancement of ontologies: 1. Clarity: explanations should be official, complete,

aimed and separate from the Social or Computational perspective. This results in reducing the number of possible understandings of an idea, thereby improving the potency of communication between providers.

2. Coherence – questions if the ontology is rationally constant. "If a phrase that can be deduced from the axioms opposes a meaning or example given informally, then the ontology is incoherent." Only implications consistent with the current explanations should be permitted.

3. Extendibility - ontologies should be developed in a way that allows for the meaning of new conditions for unique uses without requiring change in the current conditions. Moreover the ontology should be developed to work as a conceptual base for a variety of expected projects. It should be possible to expand the ontology without changing the current explanations. If this is the situation, it is not necessary to have a vocabulary sufficient to show the information relevant to all anticipated additions since the systems to determine the need of specialization are already in place.

4. Minimum Encoding Bias - Ontologies should be developed at the "knowledge level" rather than obligating the ontology to a particular execution language and its particular restrictions. Representation options should not be dependent on the comfort of a particular notation or implementation issues at the symbol-level.

5. Minimal Ontological Commitment ontologies should create as few statements as possible about the domain being patterned without losing the functionality of the ontology. Accordingly, the most fragile concept necessary to facilitate the communication consistent with the conceptualization should be specified. This allows providers to quickly increase the ontology where required for their personal requirements.

VIII. Ontology Building Three stages of building an ontology enterprise are

recommended by references [51], [52] as shown in Fig. 1.

Fig. 1. Ontology building stages

Capture At this stage, a process of identifying key concepts

and relationships in the domain is provided. Furthermore, producing precise and explicit content

definitions is mandatory for each model that can easily refer back to the same model. Model identification is a challenging task in the development of any ontology model and there is no general procedure that can be applied for all models. The data bases that are used for any ontology model are qualitative. For this reason, model identification is necessary in order to analyze this qualitative data.

Therefore, a qualitative analysis tool (NVivo), can be used for analyzing the initial concepts and relationships of the ontology model. Coding

After the model identification of ontology, coding is an essential stage which involves an explicit representation of the ontology concept.

The main purpose of coding is to control the possible misinterpretations of the key concept. Moreover, concepts are based on a hierarchical architecture such as class-superclass and instance-class [53].

There are some typical representations available for ontology documentation such as Web Ontology Language (OWL), KIF, Cyc, Ontolingua and FLogic [54]. In reference [51], the authors applied OWL for their ontology representation. OWL coding provides multiple purposes of developed disambiguation, relationship structuring, version management and a foundation. Integration with Existing Ontologies

The integration of ontologies is the process of developing new ontologies based on one or more existing relevant ontologies [56]. However, the integration of existing models typically involves aggregating, combining and assembling together the source ontologies with the newly developed ontology, extension, specialization and adaptation [55].

Up to date, there is no existing ontology which has been totally built without depending on a relevant earlier ontology. Furthermore, researchers are still encouraged to develop existing ontologies for better performance and to increase efficiency.

IX. Conclusion and Future Work Ontologies are expected to increasingly appear in

various areas as promising tools to improve communication among people and to achieve interoperability among systems.

They also act as improving agents for humans or software by reusing a known data model or Knowledge. All these tasks deal with interoperability issues and can be applied in different domains, in software development process.

This study is a systematic literature review of several ideas about the ontology, concerning ontology types and

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how to build an ontology. Additionally a description of the main components involved in ontological design has been given including a brief overview of ontology and ontology languages, ontology structure. In this paper The researcher identified more than 70 paper on this study but only 46 of them was precisely, moreover we analysis those papers and dusicused the result. In the future the researcher can analyze and implement the steps in empirical using protégé software to develop ontology by make new design or built new model.

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Authors’ information Associate Professor Dr. Marzanah A. Jabar is currently working as a Lecturer in the Department of Software Engineering and Information System. She holds a PhD in Management Information System from the Universiti Putra Malaysia, Malaysia. The topic of her doctoral dissertation was in the field of Knowledge Management. She also possesses

her M.Sc. (Computer Science) from the same university. Prior to her appointment as a Senior Lecturer in 2008, she worked as a System Analyst in UPM since 1986. Her current research interests are in Software Engineering, Knowledge Management, Information Management Systems, and Enterprise Software Development. She is currently pursuing several research projects funded by UPM and the Ministry of Higher Education.

Mustafa S. Khalefa He is currently working as PHD student in University Putra Malaysia department of software engineering and information system. He holds his Master Degree from University Technology Malaysia(UTM) (Computer Science) 2009. The topic of his dissertation was in field of software engineering. He is worked as lecturer in the

University of Basrah in Iraq education faculty department of Computer Science from 2009 until 2012. Currently he is PHD student in University of Putra Malaysia (UPM).

Prof. Dr. Rusli bin Abdullah is currently working as a Lecturer in the Department of Software Engineering and Information System. He holds PHD in Computer Science From the University Technology Malaysia (UTM) 2005. He also process his master degree M.Sc.(Computer Science) from University Putra Malaysia (UPM) 1996. He worked now as

Deputy Dean (Academic & Student Affairs) in the University Putra Malaysia. His currently working in several research and project in information system and knowledge management.

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Dr. Salfarina binti Abdullah is currently working as lecturer in the department of software engineering and information system. She holds a PHD in Management Information System 2012 from the University Putra Malaysia (UPM). She also processes her Master Degree M.Sc. (Management Information System) from the same University 2006. She is currently

working in research in information system and knowledge management.

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International Review on Computers and Software (IRECOS)

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Autorizzazione del Tribunale di Napoli n. 59 del 30/06/2006

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