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322 ENTERPRISE INFORMATION SYSTEMS VI � A format representing the resource format for display purposes. For instance, the type of movie, audio, text to know the platform and requirement for displaying it to the user. � An INSP name, in case the user wants to come back to this particular INSP later on. (Part of the action history in the user profile) The results discovered are rated using a simplistic mechanism as follows: 1: an arbitrary base score (30%) is given for all results (since it has been found, matching a minimum of one keyword). 2: a score is given for each keyword found in the title (20%). 3: a score is given for each keyword found in the description (6%). 4: a bonus score is given if the title or description contains more than one keyword (10 and 5%). 5: a score is given for the preference level at which the result was found (3%). A greater number of preferences matched, means a better, more personalised result for the user. 5 CONCLUSION AND FUTURE WORK As the number of resources on the Internet continues to grow, user faces the issue of information overload. In this situation, reliable search tools that enable personalisation of resource discovery and information retrieval becomes crucial. This is specifically true in the E-Learning domain, where users (students or researchers) have a clear need to retrieve information relevant to their preferences (language, interest, platforms, etc). Simple textbased search engines are not sufficient anymore. Several improvements have been made in the past few years showing a better search accuracy. For instance, Google (Page, 1998) now provides a new improved HTML based search engine with regard to quicker delivery and better search accuracy. Although such tools are successful, it is only a partial solution, it only delays the problem as more and more information (educational, recreational, etc) becomes available and as the number users still increases. A complete solution to this problem is the combination of current and emerging technologies and standards to offer better interaction between the user and federated search strategies. This solution, presented in this paper, has been researched and implemented by the author in the E-Learning context of the GUARDIANS European project as part of its Mediation Service component. It offers a very efficient search facility that enhances the user’s learning experience through the usage of smart functionalities (components and resources) for discovery and retrieval of educational resources. Although this work is based on E-Learning resources, one could easily fit it into the overall context of information retrieval on the Internet. Although the search strategy using XPath performed successfully, it was felt that the queries were limited with regards to performance and flexibility. Indeed, to incorporate most of the user preferences, the XPath was complicated and major results reformatting had to be performed. For this reason this initial work was extended and an XML querying solution using XQuery (Boag et al., 2002) was proposed in (Rousseau, Leray, 2003). Another important point to consider is that the matching between user profile and metadata was limited but it would be possible to include more information about the user. For example, the search could use the affiliation to only search for INSPs with which the user is affiliated. It could also use qualification details to only retrieve information relevant to the user’s previous qualification, diplomas and other licenses or certifications. However, the user profile might still be extended with new features according to future development in the E-Learning community. The ranking algorithm implementation was an interesting part of the work. However, with time constraints and the lack of information about how to actually develop a precise ranking mechanism. It was first developed as a basic algorithm, only taking care of the keywords. For a possible commercialisation purpose, it should have taken into account a few more feature from the user profile, specifically provide a complex algorithm using weighting attributes. It is expected that the authors will be involved into future research in the area of stopwords, tf.ifd, coordination level, etc (Baeza- Yates, 1999). Nowadays, there is a lot of hype around agents. In the context of this work, an agent would enable automatic discovery of the user’s platform capabilities. This issue was encountered while populating the user profile with some device information. Hence, a typical user would be unaware of its device(s) capabilities and will require an automatic detection mechanism. Also, a good balance between static and dynamic profile information should be researched further to present the user with a reliable profile that requires the minimum efforts to fill in. A form of interaction between the profiling mechanism and the user is therefore crucial to get some feedbacks to the system. The abstract representation of data on the World Wide Web, also known as the Semantic Web
PERSONALISED RESOURCE DISCOVERY SEARCHING OVER MULTIPLE REPOSITORY TYPES (Berners et al., 2001), is gradually taking over the current HTML based Web. In the near future, the development in the area will lead in significant new possibilities to enhance and process the data that they merely display at present. Hence, programs will be able to exchange information with others and automate services. Such functionalities together with the flexibility of XML will be promoted using agents, that will help users tracking down resources and match them against their criteria and preferences. REFERENCES Baeza-Yates, R. and Ribeiro-Neto, B. Modern Information Retrieval, Addison-Wesley, ACM Press, 1st edition (May 1999). ISBN: 020139829X Berners-Lee T., Hendler J. and Lassila O. The Semantic Web. A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American, May 17, 2001. Boag, S., Chamberlin, D., Fernandez, M.F., Florescu, D., Robie, J., Simeon, J., and Stefanescu, M., eds 2002. XQuery 1.0: An XML Query Language. W3C Working Draft, http://www.w3.org/TR/2002/WD-xquery- 20020430/ Bradley, N., September 7, 2000. The XSL Companion. Addison-Wesley Pub Co; 1st edition. ISBN: 0201674874 Chang, B., Scardina, M., Karun, K., Kiritzov, S., Macky, I., Novoselsky, A. and Ramakrishnan, N., 2000. ORACLE XML Handbook. Oracle Press. ASIN: 007212489X Clark, J., and DeRose, S. eds 1999. XML Path Language (XPath) version 1.0. W3C Recommendation. http://www.w3.org/TR/xpath Day, M., Summer 2001. Metadata in a nutshell. Article published in Information Europe, p. 11. Information Europe quarterly magazine of EBLIDA. Florescu, D., and Kossmann, D., 1999. A Performance Evaluation of Alternative Mapping Schemes for Storing XML Data in a Relational Database. Technical report no. 3680, INRIA, Le Chesnay Cedex, France. GESTALT, Getting Educational Systems Talking Across Leading-Edge Technologies, EU project (started in October 1998) Web Site: http://www.fdgroup.com/gestalt/ GUARDIANS project, 2000. Gateway for User Access to Remote Distributed Information and Network Services, started in October 2000. Web site: http://www.ist-guardians.tv/ Howes, T., Yeong, W. and Kille, S, 1995. Lightweight Directory Access Protocol (LDAP). The Internet Society, RFC 1770. 323 IEEE Learning Technology Standards Committee WG12: Learning Object Model, 6 March 2002. http://ltsc.ieee.org/wg12/ IEEE PAPI, 01 February 2002. IEEE Public and Private Information draft 8 specifications. Available at: http://www.edutool.com/papi/ IMS LIP version 1.00, 18 March 2001. Learner Information Package Specification. Available at: http://www.imsglobal.org/profiles/index.cfm IMS Learning Resource Metadata Specification, 01 October 2001. http://www.imsglobal.org/metadata/index.cfm ISO/IEC 11179 Part 6, 1997. Information technology - Specification and standardization of data elements - Part 6: Registration of data elements. Available online at: http://www.iso.ch/ Kanne, C.-C., and Moerkotte, G., 2000. Efficient Storage of XML Data. Proceeding of the International Conference on Data Engineering. San Diego, California, p. 198. MathML, W3C math specification, 21 February 2001, http://www.w3.org/Math MoDL - Molecular Dynamics Language project, 29 July 1999, http://xml.coverpages.org/modl.html Page, L., Brin, S. The anatomy of a large-scale hypertextual Web search engine, Computer Networks and ISDN Systems, Volume 30, Issues 1-7, April 1998, Pages 107-117. RDS. Distributed Systems Technology Centre: Research Data Unit. Resource Discovery – A Definition. Available from http://www.dstc.edu.au/RDU/rd_define.html Rousseau, B., Browne, P., Malone, P., ÓFoghlú, M., 2003. User Profiling for Content Personalisation in Information Retrieval. In the ACM Symposium on Applied Computing, special track on Information Access and Retrieval. ACM Press Rousseau, B., Leray, E., ÓFoghlú, M., 2003. Metadata & Information Management issues in XML-based Mediation. In ICEIS’03 Fifth International Conference on Enterprise Information Systems, Angers, FRANCE, pages 375-379. ICEIS Press. RSLP. Research Support Libraries Programme British initiative, September 1999, http://www.rslp.ac.uk/ Spark, K. Jones, Artificial Intelligence: What can it offer to Information Retrieval. In Proceedings of the Informatics 3, Aslib, ed., London, 1978. Suryanarayana, L., Hjelm, J., May 7-11, 2002. Profiles for Situated Web. Johan. In WWW 2002, Honolulu, Hawaii, USA. ACM Press 1-58113-449-5/02/0005. TV Anytime forum, formed in 1999. Web site, available at: http://www.tv-anytime.org/ UKOLN, centre of expertise in digital information management, University of Bath, England. http://www.ukoln.ac.uk/
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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2 ENTERPRISE INFORMATION SYSTEMS VI
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10 ENTERPRISE INFORMATION SYSTEMS V
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EVOLUTIONARY PROJECT MANAGEMENT: MU
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MANAGING COMPLEXITY OF ENTERPRISE I
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ASSESSING EFFORT PREDICTION MODELS
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ORGANIZATIONAL AND TECHNOLOGICAL CR
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ASAP Processes W Project Kickoff A
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since it means changing the relevan
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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Hit Rate (%) ACME-DB: AN ADAPTIVE C
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5.3.6 Effect on all Policies by Int
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ACKNOWLEDGEMENTS We would like to t
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This paper describes the data sampl
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The major limit A of the operation
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RELATIONAL SAMPLING FOR DATA QUALIT
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TOWARDS DESIGN RATIONALES OF SOFTWA
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((Brown et al., 1998), pp. 159-190,
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sive data filtering, presentation (
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• implementation changes in servi
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MEMORY MANAGEMENT FOR LARGE SCALE D
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Data Rate [bytes/sec] 1600000 14000
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E.g., DV Camcorder Camera Packets (
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Procedure CheckOptimal (n rs 1 ...n
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MEMORY MANAGEMENT FOR LARGE SCALE D
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PART 2 Artificial Intelligence and
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110 ENTERPRISE INFORMATION SYSTEMS
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DYNAMIC MULTI-AGENT BASED VARIETY F
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AN EXPERIENCE IN MANAGEMENT OF IMPR
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ANALYSIS AND RE-ENGINEERING OF WEB
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Module p0 Customer Itinerary Send I
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departments: the marketing dept. se
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• An acyclic module M is usable,
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BALANCING STAKEHOLDER’S PREFERENC
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The scenarios will provide an abstr
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BALANCING STAKEHOLDER'S PREFERENCES
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BALANCING STAKEHOLDER'S PREFERENCES
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A POLYMORPHIC CONTEXT FRAME TO SUPP
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A POLYMORPHIC CONTE XT FRAME TO SUP
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FEATURE MATCHING IN MODEL-BASED SOF
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combination of solution domains - a
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• features for all the concepts:
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Existence In both steps it is possi
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matching strategies are maximal, mi
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TOWARDS A META MODEL FOR DESCRIBING
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elementary message (ae-message) can
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problems on a pragmatic level, whic
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TOWARDS A META MODEL FOR DESCRIBING
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INTRUSION DETECTION SYSTEMS USING A
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INTRUSION DETECTION SYSTEMS USING A
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(k� 1) (k) (k�1) (k) p � w
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Table 5: Performance Comparison of
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A USER-CENTERED METHODOLOGY TO GENE
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carries out the second phase of the
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1 1 1 1 2 PROCESS STORE ENTITY EDGE
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constraints rules. KOGGE (Ebert et
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PART 4 Software Agents and Internet
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