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1、eBusiness Community Model - Research & Assessment Project, eBCM-RAPEnn unapuu EstonianApplicant: Gurn Rgnvaldardttir, General Manager, Icelandic Standards on behalf of Icelandic Consortium, ETeB, , +354 5207150nProject Manager: Rnar Mr Sverrisson, Project Manager, Icelandic Standards on behalf of Ic
2、elandic Consortium, ETeB, , +354 8970713nLaugavegi 178, IS-105, REYKJAVIK, IcelandProblem descriptionnAims to develop, pilot, implement and benchmark a methodical, integrated and sustainable approach for advancing B2B eBusiness based on real consensus and commitment by the participating national par
3、tners, and to avoid ad hoc initiatives, duplication of efforts, and fragmentation of resources which have characterized various developments in Europe to date. ObjectivesnObj 1: To develop a common model for eBusiness based on a multi-disciplinary, holistic approach, which takes into account all the
4、 dimensions and complexity of eBusiness in different environments from conception through proof-of-concept to actual implementation. nObj 2: To benchmark national eBusiness developments. nObj 3: To define, develop and establish architectures, methods and tools (the eBCM infrastructure) in support of
5、 the implementation of eBCM and the corresponding benchmarks. nObj 4: Validation of eBCM and the benchmarks, based on the implementation of pilot projects run within the ETeB framework.Milestonesn1) Consensus on the definitions, benchmarks and performance indicators of the eBCM as well as an approva
6、l of an initial research design. The expected completion time is June 2004.n2) Preliminary research (Research #1) concluded in 5 countries in December 2004.n3) Adjustment of research design, based on the experience acquired in Research #1 and other parallel ETeB projects, and a second research (Rese
7、arch #2) before December 2005.n4) Forming of a generic eBCM to be used in ETeB context before the end of June 2006.n5) Assessment of eBusiness status in 5 countries based on a generic model and publication of results in an official briefing meeting before November 2006.MethodologynMap - VisionnResea
8、rch Design methodolgynData Review materialnInformation EnhancementnKnowledge- Control systemnPractical knowledge optimizationnApplied knowledge - GeneralizationElektronkaubanduse mudelite klassifikatsioonInternational Journal of Electronic markets 1998 vol 8 no 2.Integreeritud funktsioonide arvmadal
9、 Innovatsiooni tase krgeE-kauplusE-hangemitmefunktsionaalneainufunktsionaalneUsaldusteenindusInfovahendusE-kaubatnavKoostarhitektuurVirtuaalsed vrtusvrgudVrtusahela teenuse pakkujaE-oksjonContentnOutlinesnWeb servicenService agentnService oriented architecturenInstructional support system architectu
10、renDecision support componentnConclusionsnQuestionsOutlinesThis presentation describes a solution to the educational systems creation: a architecture and methodology of educational system creation based on web services, service agents and decision support componentWeb servicenSoftware services are d
11、iscrete units of application logic that expose message-based interfaces suitable for being accessed across a network.nInteroperability ws-i.org nBasic profile standardsnSOAPnWSDLnUDDIService agentA service agent is a service that helps you work with other services. Often supplied by the provider of
12、the target service, the agent runs topologically close to the application consuming the service. It helps both to prepare requests to a service and to interpret responses from the service.Service oriented architecturenService oriented architecture is one where application is cut on the pieces called
13、 servicespEach service is invoked by messagingpThe semantics of the operations are around business functionsSystem architectureInstructional support system architectureDecision support componentOur goal is to find best web service among multiple offerings. Decision support component represent a poss
14、ible solution for the problem. In this component the multi-criteria analysis methods are implemented. In this approach, we consider service execution statistics, service availability statistics and consumer preferences as input for the multi-criteria analysis techniques. Multi-criteria analysisnA st
15、andard feature of multi-criteria analysis is a performance matrix, or consequence table, in which each row describes an option and each column describes the performance of the options against each criterion. The individual performance assessments are often numerical, but may also be expressed as bul
16、let point scores, or colour coding. Methods of multi-criteria analysisnLinear additive model nThe Analytical Hierarchy ProcessnOutranking method Linear additive modelnThe linear model shows how an options values on the many criteria can be combined into one overall value. This is done by multiplying
17、 the value score on each criterion by the weight of that criterion, and then adding all those weighted scores together. The Analytical Hierarchy ProcessnThe Analytic Hierarchy Process (AHP) also develops a linear additive model, but, in its standard format, uses procedures for deriving the weights a
18、nd the scores achieved by alternatives which are based, respectively, on pair wise comparisons between criteria and between options. Outranking methodnThe methods that have evolved all use outranking to seek to eliminate alternatives that are, in a particular sense, dominated. Dominance within the o
19、utranking frame of reference uses weights to give more influence to some criteria than others. Results of using multi-criteria methodsnFor the choosing the best option all mentioned methods are more all less equalsnFor the practical point of view the combinition of methods gets best resultsnThe data
20、 are more important than methodsConclusionsnWeb services and Service agent based interoperability enables the creation of the scalable and flexible global educational systemsnTo create global educational systems we need a modular suite of specifications that enables enterprises of any size and in any geographical location to conduct interactions over the Internet. nIMS Global learning Consortium offers such specificationsnMulti-criteria analysis methods makes possible to make our systems more intelligent Questions?
