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Intelligent Web Applications (Part 1) Course Introduction

Intelligent Web Applications (Part 1) Course Introduction. Vrije Universiteit Amsterdam, Fall 2002. Vagan Terziyan AI Department, Kharkov National University of Radioelectronics / MIT Department, University of Jyvaskyla vagan@it.jyu.fi ; terziyan@yahoo.com

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Intelligent Web Applications (Part 1) Course Introduction

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  1. Intelligent Web Applications (Part 1)Course Introduction Vrije Universiteit Amsterdam, Fall 2002 Vagan Terziyan AI Department, Kharkov National University of Radioelectronics / MIT Department, University of Jyvaskyla vagan@it.jyu.fi ; terziyan@yahoo.com http://www.cs.jyu.fi/ai/vagan/index.html +358 14 260-4618

  2. Contents • Course Introduction • Lectures and Links • Course Assignment • Examples of course-related research

  3. Course (Part 1) Formula:Web Personalization + Web Mining ++ Semantic Web + Intelligent Agents == Intelligent Web Applications - Why ? - To be able to intelligently utilise huge, rich and shared web resources and services taking into account heterogeneity of sources, user preferences and mobility. - What included ? - Introduction to Web content management. Web content personalization. Filtering Web content. Data and Web mining methods. Multidatabase mining. Metamodels for knowledge management. E-services and their management in wired and wireless Internet. Intelligent e-commerce applications and mobility of users. Information integration of heterogeneous resources.

  4. Practical Information • 9 Lectures (2 x 45 minutes each, in English) during period 28 October - 15 November according to the schedule; • Course slides:available online plus hardcopies; • Practical Assignment (make PowerPoint presentation based on a research paper and send electronically to the lecturer until 10 December); • Exam - there will be no exam. Evaluation mark for this part of the course will be given based on the Practical Assignment

  5. Introduction:Semantic Web - new Possibilities for Intelligent web Applications

  6. Motivation for Semantic Web

  7. Semantic Web Content: New “Users” applications agents

  8. Some Professions around Semantic Web AI Professionals Content creators Content Logic, Proof and Trust Mobile Computing Professionals Web designers Ontologies Agents Annotations Ontology engineers Software engineers

  9. Semantic Web: Resource Integration Semantic annotation Shared ontology Web resources / services / DBs / etc.

  10. What else Can be Annotated for Semantic Web ? External world resources Web resources / services / DBs / etc. Web users (profiles, preferences) Shared ontology Web agents / applications Web access devices

  11. Word-Wide Correlated Activities Semantic Web Agentcities is a global, collaborative effort to construct an open network of on-line systems hosting diverse agent based services. Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation Agentcities Grid Computing Wide-area distributed computing, or "grid” technologies, provide the foundation to a number of large-scale efforts utilizing the global Internet to build distributed computing and communications infrastructures. FIPA FIPA is a non-profit organisation aimed at producing standards for the interoperation of heterogeneous software agents. Web Services WWW is more and more used for application to application communication. The programmatic interfaces made available are referred to as Web services. The goal of the Web Services Activity is to develop a set of technologies in order to bring Web services to their full potential

  12. University of Jyvaskyla Experience:Examples of Related Courses

  13. IWA Course (Part 1): Lectures

  14. Lecture 1: Web Content Personalization Overview http://www.cs.jyu.fi/ai/vagan/Personalization.ppt

  15. Lecture 2: Collaborative Filtering http://www.cs.jyu.fi/ai/vagan/Collaborative_Filtering.ppt

  16. Lecture 3: Dynamic Integration of Virtual Predictors http://www.cs.jyu.fi/ai/vagan/Virtual_Predictors.ppt

  17. Lecture 4: Introduction to Bayesian Networks http://www.cs.jyu.fi/ai/vagan/Bayes_Nets.ppt

  18. Lecture 5: Web Mining http://www.cs.jyu.fi/ai/vagan/Web_Mining.ppt

  19. Lecture 6: Multidatabase Mining http://www.cs.jyu.fi/ai/vagan/MDB_Mining.ppt

  20. Lecture 7: Metamodels for Managing Knowledge http://www.cs.jyu.fi/ai/vagan/Metamodels.ppt

  21. Lecture 8: Knowledge Management http://www.cs.jyu.fi/ai/vagan/Knowledge_Management.ppt

  22. Lecture 9: E-Services in Semantic Web http://www.cs.jyu.fi/ai/vagan/E-Services.ppt

  23. IWA Course (Part 1): Practical Assignment

  24. Practical assignment in brief • Students are expected to select one of below recommended papers, which is not already selected by some other student, register his/her choice from the Course Assistant and make PowerPoint presentation based on that paper. The presentation should provide evidence that a student has got the main ideas of the paper, is able to provide his personal additional conclusions and critics to the approaches used.

  25. Evaluation criteria for practical assignment • Content and Completeness; • Clearness and Simplicity; • Discovered Connections to IWA Course Material; • Originality, Personal Conclusions and Critics; • Design Quality.

  26. Format, Submission and Deadlines • Format: PowerPoint ppt. (winzip encoding allowed), name of file is student’s family name; • Presentation should contain all references to the materials used, including the original paper; • Deadline - 10 December 2002; • Files with presentations should be sent by e-mail to Vagan Terziyan (terziyan@yahoo.com AND vagan@it.jyu.fi); • Notification of evaluation - until 15 December.

  27. Papers for Practical Assignment (1) • Paper 1:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_1_P.pdf • Paper 2:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_2_P.pdf • Paper 3:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_3_CF.ps • Paper 4:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_4_CF.pdf • Paper 5:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_5_MW.pdf • Paper 6:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_6_BN.ps • Paper 7:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_7_BN.pdf • Paper 8:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_8_MM.pdf

  28. Papers for Practical Assignment (2) • Paper 9:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_9_WM.ps • Paper 10:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_10_WM.pdf • Paper 11:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_11_III.pdf • Paper 12:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_12_III.pdf • Paper 13:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_13_KM.pdf • Paper 14:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_14_ES.pdf • Paper 15:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_15_MDB.pdf • Paper 16:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_16_MDB.pdf

  29. University of Jyvaskyla Experience: Examples of Course-Related Research

  30. Mobile Location-Based Service in Semantic Web

  31. Mobile Transactions Management in Semantic Web

  32. P-Commerce in Semantic Web Terziyan V., Architecture for Mobile P-Commerce: Multilevel Profiling Framework, IJCAI-2001 International Workshop on "E-Business and the Intelligent Web", Seattle, USA, 5 August 2001, 12 pp.

  33. Semantic Metanetwork for Metadata Management Semantic Metanetwork is considered formally as the set of semantic networks, which are put on each other in such a way that links of every previous semantic network are in the same time nodes of the next network. In a Semantic Metanetwork every higher level controls semantic structure of the lower level. Terziyan V., Puuronen S., Reasoning with Multilevel Contexts in Semantic Metanetworks, In: P. Bonzon, M. Cavalcanti, R. Nossun (Eds.), Formal Aspects in Context, Kluwer Academic Publishers, 2000, pp. 107-126.

  34. Petri Metanetwork for Management Dynamics • A metapetrinet is able not only to change the marking of a petrinet but also to reconfigure dynamically its structure • Each level of the new structure is an ordinary petrinet of some traditional type. • A basic level petrinet simulates the process of some application. • The second level, i.e. the metapetrinet, is used to simulate and help controlling the configuration change at the basic level. Terziyan V., Savolainen V., Metapetrinets for Controlling Complex and Dynamic Processes, International Journal of Information and Management Sciences, V. 10, No. 1, March 1999, pp.13-32.

  35. Bayesian Metanetwork for Management Uncertainty Terziyan V., Vitko O., Bayesian Metanetworks for Mobile Web Content Personalization, In: Proceedings of 2nd WSEAS International Conference on Automation and Integration (ICAI’02), Puerto De La Cruz, Tenerife, December 2002.

  36. Multidatabase Mining based on Metadata Puuronen S., Terziyan V., Logvinovsky A., Mining Several Data Bases with an Ensemble of Classifiers, In: T. Bench-Capon, G. Soda and M. Tjoa (Eds.), Database and Expert Systems Applications, Lecture Notes in Computer Science, Springer-Verlag, V. 1677, 1999, pp. 882-891.

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