Query recommendations for OLAP discovery-driven analysis

International Journal of Data Warehousing and Mining

Volumn 7, Issue 2, 2011, Pages 1-25

  Giacometti, Arnaud ^a   Marcel, Patrick ^a   Negre, Elsa ^a   Soulet, Arnaud ^a  

^a UNIVERSITÉ DE TOURS   (France)

Author keywords

Collaborative filtering; Discovery driven analysis; MDX queries; OLAP; Query recommendation

Indexed keywords

COLLABORATIVE FILTERING; DISCOVERY-DRIVEN ANALYSIS; MDX QUERIES; OLAP; QUERY RECOMMENDATIONS;

OPEN SYSTEMS; QUERY LANGUAGES;

INFORMATION RETRIEVAL;

EID: 80052897968     PISSN: 15483924     EISSN: 15483932     Source Type: Journal    
DOI: 10.4018/jdwm.2011040101     Document Type: Conference Paper

References (27)

1. Abiteboul, S., Hull, R., & Vianu, V. (1995). Foundations of Databases. Reading, MA: Addison-Wesley.
2. Adomavicius, G., & Tuzhilin, A. (2005). 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, 17(6), 734-749. doi:10.1109/TKDE.2005.99 (Pubitemid 40860454)
3. th ACM International Workshop on Data Warehousing and OLAP (pp. 9-18). New York, NY: ACM.
4. th International Conference on Data Warehousing and Knowledge Discovery (LNCS 4654, pp. 123-134).
5. th International Conference on Data Warehousing and Knowledge Discovery (LNCS 5182, pp. 33-44).
6. st International Conference on Scientific and Statistical Database Management (LNCS 5566, pp. 3-18).
7. th ACMConference on Information and Knowledge Management (pp. 449-458). New York, NY: ACM.
8. th ACM International Workshop on Data Warehousing and OLAP (pp. 73-80). New York, NY: ACM.
9. th International Conference on Data Warehousing and Knowledge Discovery (LNCS 5691, pp. 453-466).
10. th ACM International Workshop on Data Warehousing and OLAP (pp. 81-88). New York, NY: ACM.
11. th International Conference on Scientific and Statistical Database Management (LNCS 5566, pp. 83-91).
12. Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., & Venkatrao, M. (1997). Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub totals. Data Mining and Knowledge Discovery, 1(1), 29-53. doi: 10.1023/A: 1009726021843 (Pubitemid 127721235)
13. th International Conference on Data Warehousing and Knowledge Discovery (LNCS 5691, pp. 467-478).
14. th Biennial Conference on Innovative Data Systems Research. Retrieved from http://www.cidrdb.org
15. Lakshmanan, L., Pei, J., & Zhao, Y (2003). QC-Trees: An efficient summary structure for semantic OLAP. In Proceedings of the 2003 ACM International Conference on Management of Data (pp. 64-75). New York, NY: ACM.
16. Microsoft. (2008). Multidimensional expressions (MDX) reference. Retrieved September 23, 2010, from http://msdn. microsoft.com/en-us/library/ ms145506.aspx
17. Ordonez, C., & Chen, Z. (2009). Evaluating statistical tests on OLAP cubes to compare degree of disease. IEEE Transactions on Information Technology in Biomedicine, 13(5), 756-765. doi:10.1109/TITB.2008.926989
18. th ACM Conference on Information and Knowledge Management (pp. 349-358). New York, NY: ACM.
19. Pentaho. (2009). Mondrian open source OLAP engine. Retrieved September 23, 2010, from http://mondrian pentaho.org
20. Sapia, C. (1999). On modeling and predicting query behaviour in OLAP systems. In Proceedings of the International Workshop on Design and Management of Data Warehouses (pp. 2.1-2.10). CEUR-WS.org.
21. Sapia, C. (2000). Promise: Predicting query behavior to enable predictive caching strategies for OLAP systems. In Proceedings of the International Conference onData Warehousing and Knowledge Discovery (LNCS 1874, pp. 224-233).
22. th International Conference on Very Large Data Bases (pp. 42-53). San Francisco, CA: Morgan Kaufmann
23. Sarawagi, S. (2009). I3: Intelligent, interactive inspection of cubes. Retrieved September 23, 2010, from http://www.cse.iitb.ac.in/~sunita/icube
24. th International Conference on Extending Database Technology (LNCS 1377, pp. 168-182). (Pubitemid 128044749)
25. th International Conference on Very Large Data Bases (pp. 531-540). San Francisco, CA: Morgan Kaufmann.
26. Spiliopoulou, M., Srivastava, J., Kohavi, R., & Masand, B. M. (2000). Web mining for e-commerce. SIGKDD Explorations, 2(2), 106-107.
27. Stefanidis, K., Drosou, M., & Pitoura, E. (2009). « You may also like » results in relational databases. In Proceedings of the 3rd International Workshop on Personalized Access, Profile Management and Context Awareness in Databases (pp. 37-42). New York, NY: ACM.