A framework for recommending OLAP queries

ABSTRACT

An OLAP analysis session can be defined as an interactive session during which a user launches queries to navigate within a cube. Very often choosing which part of the cube to navigate further, and thus designing the forthcoming query, is a difficult task. In this paper, we propose to use what the OLAP users did during their former exploration of the cube as a basis for recommending OLAP queries to the user. We present a generic framework that allows to recommend OLAP queries based on the OLAP server query log. This framework is generic in the sense that changing its parameters changes the way the recommendations are computed. We show how to use this framework for recommending simple MDX queries and we provide some experimental results to validate our approach.

REFERENCES

• 1: T. Abeel. Java machine learning library. Available at http://sourceforge.net/projects/java-ml, 2008.
• 2: Gediminas Adomavicius , Alexander Tuzhilin, Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, IEEE Transactions on Knowledge and Data Engineering, v.17 n.6, p.734-749, June 2005 [doi>10.1109/TKDE.2005.99]
• 3: R. A. Baeza-Yates, C. A. Hurtado, and M. Mendoza. Query recommendation using query logs in search engines. In EDBT Workshops, pages 588--596, 2004.
• 4: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 5: Ladjel Bellatreche , Arnaud Giacometti , Patrick Marcel , Hassina Mouloudi , Dominique Laurent, A personalization framework for OLAP queries, Proceedings of the 8th ACM international workshop on Data warehousing and OLAP, November 04-05, 2005, Bremen, Germany [doi>10.1145/1097002.1097005]
• 6: R. W. Hamming. Error detecting and error correcting codes. Syst. Tech. J., 29:147--160, 1950.
• 7: F. Hausdorff. Grundzüge der Mengenlehre. von Veit, 1914.
• 8: L. Kaufmann and P. J. Rousseeuw. Clustering by means of medoids. In Y. Dodge, editor, Statistical Data Analysis based on the L1 Norm, pages 405--416. Elsevier/North Holland, Amsterdam, 1987.
• 9: Jon M. Kleinberg, Authoritative sources in a hyperlinked environment, Journal of the ACM (JACM), v.46 n.5, p.604-632, Sept. 1999 [doi>10.1145/324133.324140]
• 10: G. Matheron. Random Sets and Integral Geometry. John Wiley and sons, 1975.
• 11: Microsoft Corporation. Multidimensional expressions (MDX) reference. Available at http://msdn.microsoft.com/en-us/library/ms145506.aspx, 2008.
• 12: Gonzalo Navarro, A guided tour to approximate string matching, ACM Computing Surveys (CSUR), v.33 n.1, p.31-88, March 2001 [doi>10.1145/375360.375365]
• 13: OLAP Council. APB-1 OLAP benchmark - release II. Available at http://www.olapcouncil.org/research/bmarkly.htm, 1998.
• 14: C. Sapia. On modeling and predicting query behavior in olap systems. In DMDW, pages 2.1--2.10, 1999.
• 15: Carsten Sapia, PROMISE: Predicting Query Behavior to Enable Predictive Caching Strategies for OLAP Systems, Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery, p.224-233, September 04-06, 2000
• 16: S. Sarawagi. User-adaptive exploration of multidimensional data. In VLDB, pages 307--316, 2000.
• 17: Jaideep Srivastava , Robert Cooley , Mukund Deshpande , Pang-Ning Tan, Web usage mining: discovery and applications of usage patterns from Web data, ACM SIGKDD Explorations Newsletter, v.1 n.2, January 2000 [doi>10.1145/846183.846188]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.7 Database Administration
          Subjects: Data warehouse and repository

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Query formulation

General Terms:
Algorithms, Design, Experimentation, Performance

Keywords:
MDX queries, OLAP analysis, recommendation