On Implicitly Discovered OLAP Schema-Specific Preferences in Reporting Tool

Open access

On Implicitly Discovered OLAP Schema-Specific Preferences in Reporting Tool

We propose content-based methods for construction of recommendations for reports in the OLAP reporting tool. Recommendations are generated based on preference information in user profile, which is updated implicitly by collecting and analyzing user activity in the reporting tool. Taking advantage of data about user preferences for data warehouse schema elements, existing reports that potentially may be interesting to the user are distinguished and recommended. The approach used for recommending reports is composed of two methods - cold-start and hot-start.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Solodovnikova D. Data Warehouse Evolution Framework. In: Proceedings of the Spring Young Researcher's Colloquium On Database and Information Systems (SYRCoDIS'07) Moscow Russia 2007 [Online] http://ceur-ws.org/Vol-256/submission_4.pdf [Accessed 20.07.2011] http://ceur-ws.org/Vol-256/submission_4.pdf

  • Kozmina N. Niedrite L. Research Directions of OLAP Personalizaton. In: Proceedings of the 19th International Conference on Information Systems Development (ISD'10) Prague Czech Republic 2010. To be published.

  • Kozmina N. Niedrite L. OLAP Personalization with User-Describing Profiles. In: Forbrig P. Günther H. (eds.) BIR 2010. LNBIP Springer Heidelberg 2010 vol. 64 pp. 188-202.

  • Zachman J. A. The Zachman Framework: A Primer for Enterprise Engineering and Manufacturing. In: Zachman International 2003.

  • The Zachman Framework™ for Enterprise Architecture [Online] http://zachmaninternational.com/2/production/C4/downloads/Zachman_Framework.pdf

  • Koutrika G. Ioannidis Y. E. Personalization of Queries in Database Systems. In: Proceedings of the 20th International Conference on Data Engineering (ICDE'04) Boston MA USA 2004 pp. 597-608.

  • Garrigós I. Pardillo J. Mazón J.-N. Trujillo J. A Conceptual Modeling Approach for OLAP Personalization. In: Laender A. H. F. (ed.) ER 2009. LNCS Springer Heidelberg 2009 vol. 5829 pp. 401-414.

  • Golfarelli M. Rizzi S. Expressing OLAP Preferences. In: Winslett M. (ed.) SSDBM 2009. LNCS Springer Heidelberg 2009 vol. 5566 pp. 83-91.

  • Giacometti A. Marcel P. Negre E. Soulet A. Query Recommendations for OLAP Discovery Driven Analysis. In: Proceedings of the 12th ACM International Workshop on Data Warehousing and OLAP (DOLAP'09) Hong Kong 2009 pp. 81-88.

  • Jerbi H. Ravat F. Teste O. Zurfluh G. Preference-Based Recommendations for OLAP Analysis. In: Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery (DaWaK'09) Linz Austria 2009 pp. 467-478.

  • Mansmann S. Scholl M. H. Exploring OLAP Aggregates with Hierarchical Visualization Techniques. In: Proceedings of 22nd Annual ACM Symposium on Applied Computing (SAC'07) Multimedia & Visualization Track Seoul Korea 2007 pp. 1067-1073.

  • Vozalis E. Margaritis K. G. Analysis of Recommender Systems Algorithms. In: Proceedings of the 6th Hellenic European Conference on Computer Mathematics and its Applications (HERCMA'03) Athens Greece 2003 pp. 732-745.

  • Resnick P. Iacovou N. Sushak et al. Grouplens: An Open Architecture for Collaborative Filtering of Netnews. In: ACM 1994 Conference on Computer Supported Cooperative Work New York NY 1994 pp. 175-186.

  • Sarwar B. M. Karypis G. Konstan J. A. Riedl J. T. Item-based Collaborative Filtering Recommendation Algorithms. In: Proceedings of the 10th International World Wide Web Conference (WWW'10) Hong Kong 2001 pp. 285-295.

  • Solodovnikova D. Building Queries on Multiple Versions of Data Warehouse. In: Haav H.-M. Kalja A. (eds) Databases and Information Systems V - Selected Papers from the 8th International Baltic Conference DBIS 2008 IOS Press 2008 pp. 75-86.

  • Object Management Group: Common Warehouse Metamodel Specification v.1 [Online] http://www.omg.org/cgi-bin/doc?formal/03-03-02

  • Solodovnikova D. Metadata to Support Data Warehouse Evolution. In: Proceedings of the 17th International Conference on Information Systems Development (ISD'08) Paphos Cyprus 2008 pp. 627-635.

  • Maidel V. Shoval P. Shapira B. Taieb-Maimon M. Ontological Content-based Filtering for Personalised Newspapers: A Method and its Evaluation. Online Information Review 2010 vol. 34 issue 5 pp. 729-756 [Online] http://www.emeraldinsight.com/journals.htm?issn=1468-4527&volume=34&issue=5 [Accessed 20.07.2011] http://www.emeraldinsight.com/journals.htm?issn=1468-4527&volume=34&issue=5

  • Salton G. McGill M.Introduction to Modern Information Retrieval. McGraw-Hill Inc. New York NY USA 1983.

  • Breese J. S. Heckerman D. Kadie C. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceeding of the 14th Conference on Uncertainty in Artificial Intelligence (UAI'98) Madison WI USA 1998 pp. 43-52.

  • Vozalis M. Margaritis K. G. Enhancing Collaborative Filtering with Demographic Data: The Case of Item-based Filtering. In: Proceedings of the 4th International Conference on Intelligent Systems Design and Applications (ISDA'04) Budapest Hungary 2004 pp. 361-366.

  • Rashid A. M. Karypis G. Riedl J. Influence in Ratings-Based Recommender Systems: An Algorithm-Independent Approach. In: Proceedings of the 5th SIAM International Conference on Data Mining Newport Beach CA USA 2005 pp. 556-560.

  • Adomavicius G. Manouselis N. Kwon Y.-O. Multi-Criteria Recommender Systems. In: Ricci F. et al. (eds) Recommender Systems Handbook Springer Springer Science+Business Media LLC 2011 Part 5 pp. 769-803.

Journal information
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 144 51 0
PDF Downloads 47 31 1