Research Directions of OLAP Personalizaton

Conference paper

Abstract

In this paper we have highlighted five existing approaches for introducing personalization in OLAP: preference constructors, dynamic personalization, visual OLAP, recommendations with user session analysis and recommendations with user profile analysis and have analyzed research papers within these directions. We have provided an evaluation in order to point out (i) personalization options, described in these approaches, and its applicability to OLAP schema elements, aggregate functions, OLAP operations, (ii) the type of constraints (hard, soft or other), used in each approach, (iii) the methods for obtaining user preferences and collecting user information. The goal of our paper is to systematize the ideas proposed already in the field of OLAP personalization to find out further possibility for extending or developing new features of OLAP personalization.

Keywords

Drilling Editing 

Notes

Acknowledgments

This work has been supported by ESF project No.2009/0216/1DP/1.1.1.2.0/09/APIA/VIAA/044.

References

  1. 1.
    Koutrika G, Ioannidis YE (2004) Personalization of queries in database systems. In: Proceedings of 20th international conference on data engineering (ICDE’04), Boston, 30 Mar–2 Apr 2004, pp 597–608Google Scholar
  2. 2.
    Garrigós I, Pardillo J, Mazón J-N, Trujillo J (2009) A conceptual modeling approach for OLAP personalization. In: Conceptual modeling—ER 2009, LNCS 5829. Springer, Heidelberg, 401–414Google Scholar
  3. 3.
    Golfarelli M, Rizzi S (2009) Expressing OLAP preferences, LNCS 5566/2009. Scientific and Statistical Database Management, Berlin/Heidelberg, pp 83–91Google Scholar
  4. 4.
    Giacometti A, Marcel P, Negre E, Soulet A (2009) Query recommendations for OLAP discovery driven analysis. In: Proceedings of 12th ACM international workshop on data warehousing and OLAP (DOLAP’09), Hong Kong, 6 Nov 2009, pp 81–88Google Scholar
  5. 5.
    Jerbi H, Ravat F, Teste O, Zurfluh G (2009) Preference-based recommendations for OLAP analysis. In: Proceedings of the 11th international conference on data warehousing and knowledge discovery (DaWaK’09), Linz, Austria, 31 Aug–Sept 2009, pp 467–478Google Scholar
  6. 6.
    Mansmann S, Scholl MH (2007) Exploring OLAP aggregates with hierarchical visualization techniques. In: Proceedings of 22nd annual ACM symposium on applied computing (SAC’07), Multimedia and visualization track, Mar 2007, Seoul, Korea, pp 1067–1073Google Scholar
  7. 7.
    Mansmann S, Scholl MH (2008) Visual OLAP: a new paradigm for exploring multidimensonal aggregates. In: Proceedings of IADIS international conference on computer graphics and visualization (MCCSIS’08), Amsterdam, The Netherlands, 24–26 July 2008, pp 59–66Google Scholar
  8. 8.
    Solodovnikova D (2007) Data warehouse evolution framework. In: Proceedings of the spring young researcher’s colloquium on database and information systems SYRCoDIS, Moscow, Russia. http://ceur-ws.org/Vol-256/submission_4.pdfGoogle Scholar
  9. 9.
    Thalhammer T, Schrefl M, Mohania M (2001) Active data warehouses: complementing OLAP with active rules. Data Knowl Eng 39(3):241–269, Dec 2001, Elsevier Science Publishers B. V., Amsterdam, The NetherlandsGoogle Scholar
  10. 10.
    Garrigós I, Gómez J (2006) Modeling user behaviour aware websites with PRML. In: Proceedings of the CAISE’06 3rd international workshop on web information systems modeling (WISM’06), Luxemburg, 5–9 June 2006, pp 1087–1101Google Scholar
  11. 11.
    Ravat F, Teste O (2009) Personalization and OLAP databases. Ann Inf Syst 3:1–22, New Trends in Data Warehousing and Data Analysis, Springer USGoogle Scholar
  12. 12.
    Bellatreche L, Giacometti A, Marcel P, Mouloudi H (2006) Personalization of MDX queries. In: Proceedings of XXIIemes journees Bases de Donnees Avancees (BDA’06), Lille, FranceGoogle Scholar
  13. 13.
    Kimball R, Ross M (2002) The data warehouse toolkit: the complete guide to dimensional modeling, 2nd edn. Wiley, New YorkGoogle Scholar
  14. 14.
    Lenz H-J, Thalheim B (2009) A formal framework of aggregation for the OLAP-OLTP model. J Universal Comput Sci 15(1):273–303MathSciNetMATHGoogle Scholar
  15. 15.
    Inmon WH (2002) Building the data warehouse, 3rd edn. Wiley Computer Publishing, New York, 428 pGoogle Scholar
  16. 16.
    Adamson C (2006) Mastering data warehouse aggregates: solutions for star schema performance. Wiley Computer Publishing, New York, 384 pGoogle Scholar
  17. 17.
    Agrawal R, Wimmers E (2000) A framework for expressing and combining preferences. In: Proceedings of the ACM SIGMOD international conference on management of data. ACM, New York, pp 297–306Google Scholar
  18. 18.
    Borzsonyi S, Kossmann D, Stocker K (2001) The skyline operator. In: Proceedings of 17th international conference on data engineering, Heidelberg, April 2001Google Scholar
  19. 19.
    Kießling W (2002) Foundations of preferences in database systems. In: Proceedings the international conference on very large databases (VLDB’02), Hong Kong, China, pp 311–322Google Scholar
  20. 20.
    Chomicki J (2003) Preference formulas in relational queries. ACM TODS 28(4):427–466CrossRefGoogle Scholar
  21. 21.
    Kießling W, Köstler G (2002) Preference SQL-design, implementation, experiences. In: Proceedings of the international conference on very large databases (VLDB’02), Hong Kong, China, pp 990–1001Google Scholar
  22. 22.
    Pu P, Faltings B, Torrens M (2003) User-involved preference elicitation. In: IJCAI’03 workshop on configuration, Acapulco, MexicoGoogle Scholar
  23. 23.
    Hafenrichter B, Kießling W (2005) Optimization of relational preference queries. In: Proceedings of the 16th Australasian database conference, ADC 2005, vol 39, Newcastle, Australia, 31 Jan–3 Feb 2005, pp 175–184Google Scholar
  24. 24.
    Kießling W (2006) Preference handling in database systems. Talk at L3S, University of Hannover, 6 Feb 2006Google Scholar
  25. 25.
    Kießling W (2005) Preference queries with SV-semantics. In: Proceedings of COMAD’05, Goa, India, pp 15–26Google Scholar
  26. 26.
    Sarawagi S (1999) Explaining differences in multidimensional aggregates. In: Proceedings of the international conference on very large databases (VLDB’99), 7–10 Sept 1999, Edinburgh, Scotland, UK, pp 42–53Google Scholar
  27. 27.
    Gauch S, Speretta M, Chandramouli A., Micarelli A (2007) User profiles for personalized information access. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web (Chap. 2), LNCS 4321. Springer, Berlin, pp 54–87Google Scholar
  28. 28.
    Kelly D, Teevan J (2003) Implicit feedback for inferring user preference: a bibliography. ACM SIGIR Forum 37(2):18–28CrossRefGoogle Scholar
  29. 29.
    Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User Adapted Interact 12(4):331–370, Kluwer Academic Publishers, NorwellMATHCrossRefGoogle Scholar
  30. 30.
    Viappiani P, Pu P, Faltings B (2002) Acquiring user preferences for personal agents. Technical report for American Association for Artificial Intelligence (AAAI Press). http://liawww.epfl.ch/Publications/Archive/Viappiani2002.pdfGoogle Scholar
  31. 31.
    Shearin S, Lieberman H (2001) Intelligent profiling by example. In: Proceedings of IUI’01, Santa Fe, New Mexico, USA, 14–17 Jan 2001, pp 145–151Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Faculty of ComputingUniversity of LatviaRigaLatvia

Personalised recommendations