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Open Source Software Engineering Theory, Intelligent Educational Tool and Research Methodology. The development of World Wide Web WWW a little more than a decade ago has caused an. information explosion that needs an Intelligent Web IW for users to easily control their. information and commercial needs Therefore engineering schools have offered a variety of IW. courses to cultivate hands on experience and training for industrial systems In this study Open. Source Software Engineering Theory OSSET project course has been designed to help students. learn theoretical concepts of IW practice advanced technical skills and discover knowledge to. solve problem Undergraduate Science Technology Engineering and Mathematics STEM. students involved in the development of innovative approaches and techniques They are able to. help solve the problems of disease misdiagnoses that medical and healthcare professionals. experience They co authored and presented numerous research papers introducing the solution. in different conferences This study provides the solution in the form of an Intelligent OSSET. using Service Oriented Architecture SOA to decrease disease misdiagnosis in healthcare. The proposed project course has become a way to establish an Intelligent Open Source Software. Engineering for Healthcare IT center in our department Results show that this new course. strengthens the capacity and quality of STEM undergraduate degree programs and the number of. overall graduate student enrollment It promotes a vigorous STEM academic environment and. increases the number of students entering STEM careers It expands the breadth of faculty and. student involvement in research and development It enhances and leverages the active. engagement of faculty technology transfer and translational research It improves and develops. new relationships between educational institutions and research funding entities to broaden the. university s research portfolio and increase funding The proposed project course is a software. engineering research methodology an educational tool and a teaching technique is needed in. future medical and health IT fields,Introduction, Last decade the researchers have designed and developed several intelligent web technologies. such as Web Mining WM and Web Services WS These technologies have become the major. courses that provide engineering graduate students with intelligent web skills Some schools. offer these courses as elective courses in undergraduate program Others recommend it as. directed study courses for undergraduate and graduate students OSSET research project has. been evolved as a key course at North Carolina Agricultural And Technological State University. and Lawrence Technological University in the State of Michigan In the fall of 2010 the course. has been offered for the first time at Lawrence Technological University as a directed study. course for undergraduate program This research project prepares students for STEM careers. using the criteria of Service Oriented Architecture SOA Artificial Intelligent Bioinformatics. Intelligent Information Retrieval Web Middleware and Server Technologies. El Bathy designed the course as a software engineering research methodology an educational. tool and a teaching technique As a research methodology the instructor addresses the. conceptual aspects of innovation and discusses the research complications associated with the. notion The instructor also outlines a list of factors said to contribute to innovation within. organizations The course is an educational tool that the instructor uses in teaching an array of. technologies This tool is an extensive workshop in which the students learn these new. technologies implement it and discover knowledge to solve problems using technical skills they. learn The teaching technique is a structure in which the development of the research project is. formed designed and managed This technique enforces the concept of software engineering It. ensures accuracy efficiency and high quality during the process of the research project analysis. design assessment implementation test maintenance and reengineering. Web Information Retrieval IR courses are being offered for both undergraduate and graduate. students in many schools such University of Arkansas University of Texas at Austin New York. University and Lehigh University Harding University offers Search Engine Development as an. elective undergraduate course for sophomores juniors and seniors The course builds a search. engine through a set of bottom up projects It also develops projects to modify an existing open. source search engine,Motivation, Researchers have often studied open source software engineering solutions for healthcare. information technology including OSCAR FreeMed TORCH and OpenEMR These solutions. have provided high quality electronic medical records practice management systems simpler. prescription writing scheduling and billing However the authors believe that these solutions. cannot entirely solve the problems of disease misdiagnosis because of its incapability to check. diagnoses with symptoms Motivated by these problems the authors propose Open Source. Software Engineering Theory Intelligent Educational Tool Increases Placement of Graduates in. STEM Related Careers The proposed theory is an automated solution to capture the challenge. of disease misdiagnosis while students learn theoretical concepts and technical skills. The consequences of disease misdiagnosis include unnecessary treatments and testing long term. stay for the patient high costs and major health risks useless resources lateness and. unreliability The causes for this challenge involve four main factors absence of open software. systems integrity inefficient information retrieval processes poor quality of clustering different. diseases relevant information and lack of information that analysts require to strategically plan. medical and healthcare industries,Course philosophy. The philosophy of this course project is based on its level In an undergraduate program an. introduction to intelligent web development course is designed and structured The course is. highly motivated forward looking students in computer science engineering education. instructional technology medical science and management After completing this course the. student are acquainted with fundamentals of Service Oriented Architecture SOA XML. schema fundamentals of Semantic Web introduction to Artificial Intelligence Search. Methodologies Service Orchestrations with Business Process Execution Language BPEL. Introduction to Web Applications development and Introduction to IT Research Methodology. In a graduate level advanced intelligent web development course is designed and structured The. course is of interest to graduate students in computer science engineering education. instructional technology medical science and management Students master new technologies. such as Business Process Execution Language Java Server Faces JSF Web Services SOAP. WSDL UDDI APIs and XML In this course we use major platforms for web application and. web services development such as Oracle Server Application OSA and Java EE Application. server along with IDEs such as JDeveloper All background material related to HTML XML. JavaScript Java SE EE and client server architecture are developed within the course itself from. scratch The course is for students who prefer hands on experience of advanced IT Applications. and research methodologies and like the thought of using real tools It is also for students who. want to be graded based on what they can do as well as what they know and the students who are. interested in writing publishing and presenting papers in scientific conferences and journals. The Software used in the course includes,Design Tool MS Office Visio Professional.
DBMS Oracle,Java jdk 1 6 0 rc windows i586 exe,Web Server Oracle Server Application. IDE Oracle JDeveloper,JDBC classes12 zip, Thus Open Source Software Engineering Theory OSSET project course is an integration of. theory and practice approaches This paper focuses on the discussion of these approaches by. providing a technical solution that can help in solving the problems of disease misdiagnosis in. healthcare, The instructor introduced concepts and approaches of technologies techniques and software. tools that are needed to complete the project The objective is to get students to be familiar with. these concepts to develop the course project The instructor divided the class into teams Each. team member had a primary task with his her team and a secondary task with other teams Each. team selected a team leader The role of team leaders was assigning a task to each team member. clarifying the procedures of each task solving problems and providing a weekly progress report. to the project manager the instructor The tasks are based on Software Development Life Cycle. SDLC phases These phases are planning implementation testing documenting Deployment. and maintenance The students trained on each of these phases. At the same time the instructor initiated IT Research Methodology that the students followed. during the development of the project The instructor presented research concepts and. approaches These include research purpose and process research classifications Institutional. Review Board IRB scientific research approach innovation research process model research. methodology and research criteria, The remainder of the paper presents the developments of the students including a new intelligent. clustering based extended Genetic Algorithm ICEGA using Service Oriented Architecture a. discussion of research challenges for two main components of ICEGA data accuracy with. Service Oriented Architecture principles and the prototype that validates the research. preliminary results and discussion of related work. Open Source Software Engineering Theory OSSET project. Automated clustering of information relies on the ability to programmatically adapt over time to. find new methodologies necessary to break data into meaningful clusters With data constantly. changing it is desired to develop an algorithm capable of clustering in a way that is relevant to. the data that is being clustered In order to tackle this problem the algorithm must have the. ability to try numerous ways of clustering a particular data set. In an attempt to allow for this capability the use of an intelligent clustering based extended. genetic algorithm has been put in place to provide a way of clustering data that is relevant to the. type of data being clustered with the ability to adapt over time to changes in subjects of topics of. desired data By developing such algorithm data can evolve into information in a way that. produces robust flexibility, Researchers have often studied general algorithms and technical types of information systems.
which cannot entirely solve these problems Therefore the authors claim that the industries. organizations still face severe obstacles mainly in clustering relevant information that have. adapted over time This claim is derived from the observation of the results of disease. misdiagnosis in medical and healthcare industries Such results include unnecessary treatments. and testing long term stay for the patient high costs and major health risks useless resources. lateness and unreliability The incidence rate of misdiagnosis is rationally ranges from 1 4 in. cancer biopsies to a high 20 40 misdiagnosis rate in emergency or ICU care Patients surveys. show that diseases misdiagnosis ranges from 8 to 40 The rate of failure to diagnose and. treat in time most common reason for a patient safety incident is 155 per 1 000 hospitalized. Current research has improved data clustering by applying different algorithms to group diseases. according to patient s symptoms However the authors claim that even if these algorithms can. find a solution faster the quality of data clustering and relevancy between symptom matching. and relevant diseases remain a challenging research problem. In this paper the problem of clustering intelligent web search engine using K means algorithm. has been analyzed and the need for a new data clustering algorithm such as Intelligent Clustering. Based Extended Genetic Algorithm ICEGA is justified to improve the process of disease. diagnosis While K means is useful and efficient when it comes to clustering data it lacks the. ability to intelligently evolve over time to user browsing patterns and collected data topics In. this paper the concept of genetic algorithm based clustering has been modified and applied to. provide better diseases clustering results in a more efficient manner. To our knowledge this work is the first optimal approach for clustering based extended genetic. algorithm ICEGA is a complementary research It does not disqualify current information. retrieval and data clustering research The goal of ICEGA is to address the applicability of. potential extended genetic algorithm to solve the efficiency and limitation problems in data. clustering To achieve this goal this course project integrated concepts and approaches of search. methodologies information extraction intelligent information retrieval clustering extended. genetic algorithm and data warehousing This project is designed and developed in a SOA. environment to enable an intelligent architecture, In this paper the authors examined a fundamental theory for ICEGA that can establish the. groundwork for more future research This theory is a new attempt to apply SOA principles by. providing dynamic services that have concrete meaning on the industries level to improve the. capability of the organizations These services enable Intelligent Information Retrieval Lifecycle. Architecture as a requirement to help solve the problems of clustering relevant data with the. ability to adapt over time, A prototype is created and examined in order to validate the concepts This project involves. collaboration with domain scientist and students to evaluate ICEGA on important scientific. computing application Also the authors collaborate with the Children s Hospital of Philadelphia. to increase the number of students and underrepresented cultural minorities in undergraduate. Intelligent clustering based extended genetic algorithm. Genetic algorithm is considered to be one of robust and efficient search and optimization. technique that was inspired by evolutionary biology and computation research Traditionally GA. uses fixed length bit string of natural selection of living organisms for representation. In our project we proposed ICEGA mechanism to be an optimal solution for data clustering to. improve the efficiency and performance for retrieving a proper information results that satisfy. our user s needs ICEGA can use several mutation operators simultaneously to produce next. generation This series of random mutation process depend on chromosome best fitness in the. population and also rely on high relevancy as well The mutation operation guarantees the. success of genetic algorithms for data clustering since it expands the search So the highly. effective mutation operators the greater effects on the genetic process Finally The ICEGA for. data clustering gives the user needed documents based on similarity between query matching and. relevant document mechanism,Data Preparation and Clustering. The purpose of our clustering algorithm is to divide set of N documents into K clusters where. the sum of distances D between clusters documents is the least possible This means that when. clustering algorithm has been completed the set will be divided into K proper subsets with no. documents in more than one such subset of the documents Each subset has the closest grouping. of documents possible with K clusters, In our clustering algorithm each document is stored both as a set of weights and a set of words. that the weights correspond to The set of weights is the ratio of each word s occurrences to the. sum of all words in the document s occurrences To simplify some of the computations involved. each document s set of words contains every word that appears in any of the other documents. but with a weight of zero if it does not actually occur within that document Euclidean distance is. utilized in computing the similarity to quantify the distance between the documents in each. cluster The average of the distances between all documents in each cluster to each other as if. they were points in an n dimensional space is used as our quality for each cluster In an n. dimensional space n is the number of words in each document. The following math is used to find D the average distance between the documents in the ith. cluster of set C of clusters, The variable P is used to hold the Cartesian product of the set of documents in the cluster with.
itself creating a set of pairs of documents Each pair in P contains two documents from within. the cluster and to find the average distance between any two documents in the cluster each. pair s distance will need to be computed The function d is the Euclidean distance between two. sets D the average distance between the documents in Ci is calculated by finding the sum of all. distances of P s elements and finding the quotient of that and the cardinality of P. In this paper the structure of genetic algorithm is extended to hold multiple populations in the. population space The ICEGA is designed using artificial intelligence methodologies not. geometric approaches to the clustering problem Our proposed method uses a genetic algorithm. to find an ideal clustering solution instead of a more mathematical method such as the K means. algorithm This key difference allows for more adaptive behavior within our clustering method. This paper builds a utility based intelligent agent that implements a faster genetic algorithm with. greater efficiency than the original algorithm The clustering process involves a series of. mutations that will evolve over time taking only mutations with a high relevancy and mutating. those further Figure 1 describes Intelligent clustering based extended Genetic Algorithm. Figure 1 The Algorithm, The fitness of an individual is computed based on the distances between the words or other. tracked items appearing within a document The items are compared by their weights meaning. the ratio of their appearances to the total sum of words in the document These weights are then. treated as if they were coordinated for the document s point on an n dimensional grid where n is. the number of different words appearing within the set of documents being clustered by ICEGA. In ICEGA algorithm an individual with a lower fitness value actually represents a solution of. greater quality than one with a greater fitness value This is because the quality of the clustering. solution is the closeness of the items being clustered Only the most individual fit is passed on to. the next generation The fitness for a chromosome is found through repetition of the math used. for finding the similarity of the documents in a cluster For each chromosome in the generation. the fitness is computed by finding the average of the similarities for each cluster By using this. method the fitness is also the average distance between any two documents in any one cluster in. the solution, Mutation is a way that changes the population to produce the best solution The ICEGA. clustering process involves a series of mutations that will evolve over time taking only the. mutations with a high relevancy and mutating those further The ICEGA algorithm used one. type of mutation This type is known as a one point mutation A single document s position is. moved through the chromosome switching its place in the clusters with another document. Through the repeated use of this type of mutations the solution can create a generation. consisting of a multitude of clustering possibilities. To further increase the genetic diversity present in each generation of the ICEGA the algorithm. includes a step where a new individual is added to the population This individual is randomly. generated with each generation iterated to create additional diversity even without the crossover. step s inclusion in the algorithm, The proposed algorithm would build new chromosomes out of sections from two different. chromosomes creating new generations with greater diversity The lesser number of generations. required comes with a cost in the form of a drop in efficiency. Chromosomes are encoded to represent a genetic algorithm and to be parsed into tree structures. Currently our genetic algorithm stores each chromosome as a sequence of characters. representing the documents The order of the characters in our chromosomes is of great. importance and no repeats are allowed Using crossovers in the source code of our genetic. algorithm negatively affects the efficiency of the algorithm more than it would lower the amount. of generations required The proposed genetic algorithm is simply a way to go through a vast. number of possible solutions with greater speed and efficiency than other strategies With or. without crossovers our genetic algorithm should arrive at the same value. Research challenge 1 Data accuracy with SOA, As it is important to manipulate data accurately and efficiently Service Oriented Architecture. approach has been proposed Because SOA is a growing successful paradigm it enables the. development of this project as smoothly integrated and reused web services The benefits of. using SOA include reduction of development time and integration costs Therefore Service. Oriented Architecture is a central part of the concept that is proposed in this project It. implements dynamic service capabilities with intelligent clustering based extended genetic. algorithm to apply reasoning and flexible service workflows. As the research focuses on the development of intelligent clustering based extended genetic. algorithm using service oriented architecture it introduces intelligent information retrieval. lifecycle architecture with the ability to adapt over time to changes in subjects of topics of. desired data Figure 2 describes the architecture, Figure 2 Information Clustering Lifecycle Architecture Based Extended Genetic Algorithm using SOA.
One specific research question which arises is How does the integration of search. methodologies intelligent information retrieval intelligent clustering extended genetic. algorithm and intelligent agents using SOA solve the efficiency and limitation problems in data. clustering In the course project the students deployed SOA middleware as a suite consisting of. Web service, A web service is a technology that enables programs to communicate through Hypertext Transfer. Protocol HTTP on the Internet The students published and consumed two web services to. perform operations that are required for developing the project The services operations include. Search extract intelligent information retrieval SEIIR web service. The first operation is Search Engine SE that searches web and local databases for a query. string The second operation is Information Extraction IE that extracts text from the source. code of web documents The third operation is Intelligent Information Retrieval IIR that. retrieves top ranked documents that are relevant to query strings This operation involves. document query representation document ranking retrieval modeling and retrieval quality. evaluation, Intelligent Clustering Based Extended Genetic Algorithm ICEGA Web Service. This service performs operations that are needed for clustering top ranked documents diseases. Once ICEGA algorithm is put in place the desired service item can be requested Upon this. initial request the first generation of information retrieval is randomly generated which can lead. to a slight decrease of efficiency What makes up for this initial sacrifice in performance is that. as the workflow processes information the algorithm creates a new generation of logic and the. results are assessed based on goodness of fit to results As new logic workflows are developed. they can be selected and mutated to produce better results As this process continues eventually. the operation IIR can be provided to matchmaking with user requirements in such a way to. enable increased efficiencies over time Upon delivery of the user request the generation cycle. is terminated,Business Process Execution Language BPEL. The orchestration of web services is supported by Business Process Execution Language. BPEL In this course project the students simply designed deployed monitored and. administered the process within a framework provided by Oracle BPEL Process Manager BPEL. enables linking SEIIR and ICEGA services as one piece of a process. Enterprise Service Bus ESB, ESB is the services loosely coupled groundwork utilizing SOA for providing improved business. flexibility reusability and largely reaction in message oriented environment applying industry. standards In this research the students implemented ESB to transform and rout intelligent. information from operational database to data warehouse. Oracle Application Service OAS, OAS is standards based software system server It enables complete platform integration for.
executing SEIIR ICEGA and Intelligent BPEL process The students deployed executed and. tested using OAS,Research challenge 2 prototype model. The prototype of the research is a simulation of the conceptual solution which can be applied in a. real world The students applied Architected Rapid Application Development ARAD prototype. model The prototype intelligent processes are Information Retrieval IR and Clustering. Extended Genetic Algorithm CEGA,Prototype projects. The students developed three types of projects Projects that provide services These services are. SEIIR Search Extraction and Information Retrieval and CEGA Clustering Extended Genetic. Algorithm IIRLABPEL project that defines flow of action in the application It invokes projects. that provide services A web front end application called the IIRLAUserInterface is provided. such that the system can be invoked by the users, The projects are invoked in the following order When a user enters a query string using the. IIRLAUserInterface application this action invokes the IIRLABPEL project The IIRLABPEL. project defines the main flow of the system The SEIIR project receives the query string and. returns query ids The CEGA project clusters the documents and writes document s ID the. query s ID and the cluster name to the database,Technologies and Techniques. The students integrated SOA Suite technologies such as BPEL to invoke web services in a. defined flow sequence Table 1 lists the technologies and techniques used in the projects The. requirements of the prototype s system are translated into an object data model The model is. transformed into object class databases that store the data Figure 3 illustrates the model. Table 1 Technologies and Techniques Used in Each Project. Technologies Web Services Tables Techniques, IMEIRLAUserInterface Shows how to invoke the ISLABPEL project from the.
Search button, IMEIRLABPEL SEIIR Shows how to use BPEL to orchestrate a flow. ICEGA sequence,Invokes the services provided by all the projects. IMEIRLAWS SEIIR query Shows bottom up implementation of web services. ICEGA DocInfoExtra starting with Java classes you use JDeveloper to. ction generate a WSDL file,stopwords Uses JDBC new internal method. ClusteringGA, In the course project a real time data warehouse using SOA is designed Variable data of. different bundled database systems are obtained and captured by a web service. Figure 3 Object Class Data Model,Prototype Walkthrough.
The techniques of walkthrough are approved as experimental assessment approaches to evaluate. system application usability The course project carried out a contextualized usability assessment. walkthrough technique that examines the prototype The walkthrough method evaluates the. different phases of the research process During the system evaluation phase the examiners. evaluated the interfaces that are related to real roles and real users. The walkthrough examiners of this study are professors researchers and SOA engineers in. North Carolina A T State University They identified different types of problems These types. include design development testing usability and maintenance problems They verified that the. prototype satisfies the requirements of this research Also the prototype is evidence that proves. the new concept is valid the solution is conceptualized and the findings answered the research. question and solved the research problem,Preliminary results. The ICEGA algorithm is tested on set of sample data The data is based on 50. generations iterations of the ICEGA or K means respectively using the same random sample set. of 15 documents with 600 words each Figure 4 serves as decent evidence that the solutions from. our Intelligent Clustering Based Extended Genetic Algorithm are generally closer clustered than. those generated by K means even if K means can find a solution faster Figure 4 defines GA 1. and GA 2 as the two graphed trials of the genetic algorithm. Figure 4 ICEGA and K Means Comparison, Figure 5 presents sample set of 15 documents as a demonstration of clustering The document set. has been simplified to only have 2 different words in each document The values on the X and Y. axes are the word weights of those two words in the documents Figure 5a shows the documents. arranged on 2 dimensional grid without any clustering information applied Figure 5b and 5c. differ in that the documents have been colored and circled to designate the different clusters. within the set of documents Figure 5b has been clustered using the K means algorithm while. with Figure 5c our genetic algorithm is used to find a clustering solution. Figure 5 a Documents without clustering left b K means Clustering Results middle c ICEGA Results right. The results are listed in Table 2 were collected over 15 test runs of both clustering methods on. the same data set The table shows the statistics collected from ICEGA and K means algorithms. to demonstrate their relative performance capabilities The values given are the fitness of the. final clustering solution generated by each run which means that the lower fitness are from. better solutions while higher fitness values are worse solutions As each method uses a random. starting point there is room for variation in solutions. From this data we can observe that on average our ICEGA algorithm excels K means clustering. algorithm The test runs did not find as good a solution with K means as the best solution from. the ICEGA algorithm and even the worst solution from the ICEGA algorithm is of better fitness. than the average solution from K means, While the data collected does not represent all possible input cases and cannot claim to represent. all of them it shows a trend of the ICEGA algorithm exceeding the performance shown the. clustering process we had used previously,Table 2 ICEGA and K means Performance. ICEGA K means,Maximum 1 66384 1 86476,Average 1 56938 1 67881.
Minimum 1 35574 1 40269, The preliminary results show that the proposed algorithm outperforms K means algorithm The. proposed concept ensures high level of accuracy and efficiency due to removal of irrelevant. information The Clustering Intelligent Extended Genetic Algorithm ICEGA enhances an. organization s ability to collect information faster at lower cost and to make accurate decisions. The orchestrations of clustering extended genetic algorithm by applying SOA principles and. concepts allow flexible service workflows to be immediately adjusted to modifications and make. systems smarter Preliminary results also show that ICEGA can discover related diseases to. doctors original diagnosis and automatically reassesses the situation if their diagnosis is. incorrect The proposed algorithm solution markedly increase the success of disease clustering. and relevancy between patient s symptoms and diseases. In addition the instructor asked the students to complete a job survey and return it once they. obtain a job in any of the areas that they worked on during the course the project Figure 6 shows. the number of jobs that offered to students in each of the skills learned in course project in the. past two years, In web service technology 160 students received job offer In SQL and XML 150 students. received job offer In SOA and BPEL 140 students received job offer In Java 120 students. received job offer,Figure 6 Number of jobs offered to the student. Related work, Previous work in data clustering has focused on concepts similar to Intelligent Clustering Based. Extended Genetic Algorithm K means is most successfully used on data sets because of its. simplicity and its linear time complexity However it is not feasible to be used on large data sets. Hierarchal clustering algorithm creates a structure that reflects the order of divided groups It. gives better results than K means if it uses random data set A GA based unsupervised clustering. technique selects cluster centers directly from the data set and allows acceleration of the fitness. evaluation via a look up table A limitation of existing techniques is the inability to adapt over. time to changes in data Such techniques do not provide a general architecture that enables any. operation to be automatically optimized for any system. Conclusion, Open Source Software Engineering Theory OSSET project course is a software engineering.
research methodology an educational tool and a teaching technique It also helps students learn. theoretical concepts practice advanced technical skills and discover knowledge to solve. problem The course satisfies the needs of undergraduate and graduate students in computer. science engineering education instructional technology medical science and management. This new course strengthens the capacity and quality of STEM undergraduate degree programs. and the number of overall graduate student enrollment It promotes a dynamic STEM academic. environment and increases the number of students entering STEM careers. Acknowledgements, The primary author of this paper Dr Naser El Bathy gratefully acknowledges the students who. enrolled in this course project for their significant contributions to achieve the goal objectives. and activities of this research,Bibliography, 1 R H Abrahiem A New generation of middleware solution for a near real time data warehousing architecture. Electro Information Technology IEEE International Conference pp 192 197 May 2007. 2 F Gomez B Chandrasekaran Knowledge organization and distribution for medical diagnosis IEEE. Transactions on Systems Man and Cybernetics Vol SMC I 1 No 1 Jan 1981. 3 R T Watson Data Management Databases and Organizatins Wiley 2006. 4 Q Gu P Lago A stakeholder driven service life cycle model for SOA ACM New York pp 1 7 2007. 5 P Kudov a Clustering genetic algorithm IEEE DOI 10 1109 DEXA 65 2007. 6 B Coppin Artificial intelligence illuminated Sudbury Massachusetts John and Bartlett Publishers 2004. 7 C Perks T Beveridge Guide to Enterprise IT Architecture New York Springer Verlag 2003. 8 J Cresswell Research Design Qualitative Quantitative and Mixed Methods Approaches Thousand Oaks. London New Delhi SAGE Publications International Educational and Professional Publisher 2003. 9 D Remenyi B Williams A Money E Swartz Doing Research in Business and Management An. Introduction to Process and Method London Thousand Oaks New Delhi SAGE Publications 2005. 10 D T Sanders J A Hamilton R A MacDonald Supporting a service oriented architecture Society for. Computer Simulation International San Diego CA pp 325 334 2008. 11 D Krafzig K Banke D Slame Enterprise SOA Service Oriented Architecture Best Practices NJ Prentice. 12 T Erl Service Oriented Architecture Afield Guide to Integrating XML and Web Services NJ Prentice Hall. 13 M P Papazoglou W Heuvel Service oriented architectures approaches technologies and research issues The. VLDB Journal Springer Berlin Heidelberg vol 16 Number 3 pp 389 415 2007. 14 D Vladimir Development of applications with service oriented architecture for grid ACM New York Vol. 15 D Steiner Oracle SOA Suite Quick Start Guide 10g 10 1 3 1 0 Oracle pp 7 11 2006. 16 M Doernhoefer Surfing the net for software engineering notes ACM SIGSOFT Software Engineering. Notes Volume 30 Number 6 Nov 2005, 17 A Dan R Johnson and A Arsanjani Information as a service modeling and realization International. Workshop on Systems Development in SOA Environments IEEE 2007. 18 R Akerkar P Lingras Building an Intelligent Web Theory and Practice Sudbury Massachusetts Jones and. Bartlett Publishers 2008, 19 N Chaiyarataiia A M S Zalzala Recent developments in evolutionary and genetic algorithms theory and. applications Genetic Algorithms in Engineering Systems Innovations and Applications GALESIA 97 Second. International Conference On Conf Publ No 446 pp 270 277 1997.

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