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Web multiform data structuring

  • J. Darmont
  • O. Boussaid
  • F. Bentayeb
  • S. Rabaseda
  • Y. Zellouf
Part of the Multimedia Systems and Applications Series book series (MMSA, volume 22)

Abstract

In the context of e-commerce, analyzing the behavior of a customer, a product, or a company consists in monitoring one or several activities (commercial or medical pursuits, patent deposits, etc.). The objective of multidimensional analysis, particularly OLAP (On-Line Analytical Processing), is to analyze such activities under the form of numerical data. The information is summarized and can be presented as relevant information (i.e., knowledge) allowing to couple OLAP with other analysis tools such as KDD (Knowledge Discovery in Databases) techniques (namely, data mining), whose objectives include understanding and predicting the behavior of one or several activities. Hence, the scope of analysis can be extended.

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References

  1. Abiteboul, S., et al. (1997). The Lorel query language for semi structured data. International Journal on Digital Libraries 1(1), 68–88.MathSciNetCrossRefGoogle Scholar
  2. Anderson, R., et al. (2000). Professional XML Databases. Wrox Press.Google Scholar
  3. Bertino, E., Catania, B., and Zarri, G.P. (2001). Intelligent Database Systems. Addison Wesley.Google Scholar
  4. Bray, T., et al., Eds. (2000). Extensible Markup Language (XML) 1.0 (Second Edition). http://www.w3.org.Google Scholar
  5. Busse, S., et al. (1999). Federated Information systems: Concepts, Terminology and Architectures. Forschungsberichte des Fachbereichs Informatik 99(9).Google Scholar
  6. Ceri, S., et al. (1999). XML-GL: a graphical language for querying and restructuring XML documents. International World Wide Web Conference, Canada.Google Scholar
  7. Chaudhuri, S., and Dayal, U. (1997). Data Warehousing and OLAP for Decision Support. ACM SIGMOD International Conference on Management of Data (SIGMOD 97), Tucson, USA, 507–508.Google Scholar
  8. Christophides, V., et al. (1994). From Structured Documents to Novel Query Facilities. ACM SIGMOD International Conference on Management of Data, Minneapolis, USA, 313–324.Google Scholar
  9. Cover, R. (2001). XML Metadata Interchange (XMI). http://xml.coverpages.org/xmi.html.Google Scholar
  10. Deutsch, A., Fernandez, M., and Suciu, D. (1999). Storing semistructured Data with STORED. ACM SIGMOD International Conference on Management of Data, Philadelphia, USA, 431–442.Google Scholar
  11. Deutsch, A., et al. (1999). XML-QL: A Query Language for XML. International World Wide Web Conference, Canada.Google Scholar
  12. Deutsch, A., et al. (1999). Querying XML Data. IEEE Data Engineering Bulletin 22(3), 10–18.Google Scholar
  13. Edmonds, A. (2001). A General Background to Supervised Learning in Combination with XML. Technical paper, Scientio Inc. http://www.metadatamining.com.Google Scholar
  14. Fallside, D.C., ed. (2001). XML Schema. http://www.w3.org.Google Scholar
  15. Fernandez, M., et al. (1998). Catching the Boat with Strudel: Experiences with a Web-Site Management System. ACM SIGMOD International Conference on Management of Data, Seattle, USA, 414–425.Google Scholar
  16. Fernandez, M., Marsh, J., and Nagy, M., Eds. (2001). XQuery 1.0 and XPath 2.0 Data Model. http://www.w3.org.Google Scholar
  17. Florescu, D., and Kossmann, D. (1999). Storing and Querying XML Data using an RDMBS. IEEE Data Engineering Bulletin 22(3), 27–34.Google Scholar
  18. Hackathorn, R. (2000). Web farming for the data warehouse. Morgan Kaufmann.Google Scholar
  19. Hopmann, A., Berkun, S., Hatoun, G. (1997). Web collections using XML. http://www.w3.org.Google Scholar
  20. Hsiao, D. (1992). Federated Databases and Systems: Part I – A Tutorial on Their DataSharing. VLDB Journal 1(1), 127–179.CrossRefGoogle Scholar
  21. Hsiao, D. (1992). Federated Databases and Systems: Part II – A Tutorial on Their Resource Consolidation. VLDB Journal 1(2), 285–310.CrossRefGoogle Scholar
  22. Inmon, W.H. (1996). Building the Data Warehouse. John Wiley & Sons.Google Scholar
  23. Kappel, G., Kapsammer, E., and Retschitzegger, W. (2000). X-Ray – Towards Integrating XML and Relational Database Systems. 19 th International Conference on Conceptual Modeling, 339–353.Google Scholar
  24. Kimball, R. (1996). The data warehouse toolkit. John Wiley & Sons.Google Scholar
  25. Kimball, R., and Mertz, R. (2000). The Data Webhouse: Building the Web-enabled Data Warehouse. John Wiley & Sons.Google Scholar
  26. McHugh, J., et al. (1997). Lore: A Database Management System for Semi-structured Data. SIGMOD Record 26(3), 54–66.CrossRefGoogle Scholar
  27. Miniaoui, S., Darmont, J., and Boussaid, O. (2001). Web data modeling for integration in data warehouses. First International Workshop on Multimedia Data and Document Engineering (MDDE 01), Lyon, France, 88–97.Google Scholar
  28. Nestorov, S., Abiteboul, S., and Motwani, R. (1998). Extracting Schema from Semistructured Data. ACM SIGMOD International Conference on Management of Data, Seattle, USA, 295–306.Google Scholar
  29. OMG. (1999). Unified Modeling Language Specification, Version 1.3. Object Management Group, Inc.Google Scholar
  30. Rakow, T.C., Neuhold, E.J., and Löhr, M. (1995). Multimedia Database Systems – The Notions and the Issues. Datenbanksysteme in Büro, Technik und Wissenschaft BTW, GI-Fachtagung, Dresden, 1–29.CrossRefGoogle Scholar
  31. Robie, J., Lapp, J., and Schach, D. (1998). XML Query Language (XQL). http://www.w3.org/TandS/QL/QL98/pp/xql.html.Google Scholar
  32. Shanmugasundaram, J., et al. (1999). Relational Databases for Querying XML Documents: Limitations and Opportunities. 25 th International Conference on Very Large Data Bases (VLDB 99), Edinburgh, Scotland, 302–314.Google Scholar
  33. Shanmugasundaram, J., et al. (2001). A General Technique for Querying XML Documents using a Relational Database System. SIGMOD record 30(3), 302–314.CrossRefGoogle Scholar
  34. Shoens, K., et al. (1993). The Rufus System: Information Organization for Semi-Structured Data. 19th International Conference on Very Large Data Bases, Dublin, Ireland, 97–107.Google Scholar
  35. Tan, A.H. (1999). Text Mining: The state of the art and the challenges. PAKDD 99 Workshop on Knowledge discovery from Advanced Databases (KDAD 99), Beijing, China, 71–76.Google Scholar
  36. Thuraisingham, B. (2001). Managing and Mining Multimedia Databases. CRC Press.Google Scholar
  37. Wu, M.C., and Buchmann, A.P. (1997). Research Issues in Data Warehousing. BTW ′ 97 Ulm.Google Scholar
  38. Zhang, J., Hsu, W., and Lee, M.L. (2001). An Information-Driven Framework for Image Mining. 12th International Conference on Database and Expert Systems Applications (DEXA 2001), Munich, Germany; LNCS (2113), 232–242.Google Scholar
  39. Zwol, R., Apers, P., and Wilschut, A. (1999). Modelling and querying semistructured data with MOA. Workshop on Query processing for semistructured data and non-standard data formats.Google Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • J. Darmont
    • 1
  • O. Boussaid
    • 1
  • F. Bentayeb
    • 1
  • S. Rabaseda
    • 1
  • Y. Zellouf
    • 1
  1. 1.ERICUniversité Lumière Lyon 2France

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