Abstract
In Business Intelligence systems, users interact with data warehouses by formulating OLAP queries aimed at exploring multidimensional data cubes. Being able to predict the most likely next queries would provide a way to recommend interesting queries to users on the one hand, and could improve the efficiency of OLAP sessions on the other. In particular, query recommendation would proactively guide users in data exploration and improve the quality of their interactive experience. In this paper, we propose a framework to predict the most likely next query and recommend this to the user. Our framework relies on a probabilistic user behavior model built by analyzing previous OLAP sessions and exploiting a query similarity metric. To gain insight in the recommendation precision and on what parameters it depends, we evaluate our approach using different quality assessments.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowl. and Data Eng. 17(6), 734–749 (2005)
Baeza-Yates, R., Hurtado, C.A., Mendoza, M.: Query recommendation using query logs in search engines. In: Lindner, W., Fischer, F., Türker, C., Tzitzikas, Y., Vakali, A.I. (eds.) EDBT 2004. LNCS, vol. 3268, pp. 588–596. Springer, Heidelberg (2004)
Khoussainova, N., Balazinska, M., Gatterbauer, W., Kwon, Y., Suciu, D.: A Case for A Collaborative Query Management System. In: CIDR 2009, Fourth Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 4-7. Online Proceedings (2009)
Stefanidis, K., Drosou, M., Pitoura, E.: You May Also Like results in relational databases. In: Proceedings International Workshop on Personalized Access, Profile Management and Context Awareness: Databases, Lyon, France (2009)
Chatzopoulou, G., Eirinaki, M., Polyzotis, N.: Query Recommendations for Interactive Database Exploration. In: Winslett, M. (ed.) SSDBM 2009. LNCS, vol. 5566, pp. 3–18. Springer, Heidelberg (2009)
Khoussainova, N., Kwon, Y., Balazinska, M., Suciu, D.: SnipSuggest: Context-Aware Autocompletion for SQL. PVLDB 4(1), 22–33 (2010)
Khoussainova, N., Kwon, Y., Liao, W.-T., Balazinska, M., Gatterbauer, W., Suciu, D.: Session-based browsing for more effective query reuse. In: Bayard Cushing, J., French, J., Bowers, S. (eds.) SSDBM 2011. LNCS, vol. 6809, pp. 583–585. Springer, Heidelberg (2011)
Drosou, M., Pitoura, E.: Redrive: result-driven database exploration through recommendations. In: Macdonald, C., Ounis, I., Ruthven, I. (eds.) CIKM, pp. 1547–1552. ACM (2011)
Sellam, T., Kersten, M.: Meet Charles, big data query advisor. In: CIDR 2013, Sixth Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 6-9. Online Proceedings (2013)
Giacometti, A., Marcel, P., Negre, E.: A framework for recommending OLAP queries. In: Proc. DOLAP, Napa Valley, CA, pp. 73–80 (2008)
Sapia, C.: PROMISE: Predicting query behavior to enable predictive caching strategies for OLAP systems. In: Kambayashi, Y., Mohania, M., Tjoa, A.M. (eds.) DaWaK 2000. LNCS, vol. 1874, pp. 224–233. Springer, Heidelberg (2000)
Howard, R.: Dynamic programming and Markov processes. Technology Press of Massachusetts Institute of Technology (1960)
Sarawagi, S., Agrawal, R., Megiddo, N.: Discovery-driven exploration of OLAP data cubes. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 168–182. Springer, Heidelberg (1998)
Sarawagi, S.: User-adaptive exploration of multidimensional data. In: Proc. VLDB, Cairo, Egypt, pp. 307–316 (2000)
Aligon, J., Golfarelli, M., Marcel, P., Rizzi, S.: Similarity measures for OLAP sessions. International Journal of Knowledge and Information Systems (to appear, 2013)
Giacometti, A., Marcel, P., Negre, E.: Recommending multidimensional queries. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2009. LNCS, vol. 5691, pp. 453–466. Springer, Heidelberg (2009)
Drosou, M., Pitoura, E.: ReDRIVE: result-driven database exploration through recommendations. In: Proc. CIKM, Glasgow, UK, pp. 1547–1552 (2011)
Yang, X., Procopiuc, C., Srivastava, D.: Recommending join queries via query log analysis. In: Proc. ICDE, Shanghai, China, pp. 964–975 (2009)
Chatzopoulou, G., Eirinaki, M., Koshy, S., Mittal, S., Polyzotis, N., Varman, J.S.V.: The querie system for personalized query recommendations, 55–60 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Aufaure, MA., Kuchmann-Beauger, N., Marcel, P., Rizzi, S., Vanrompay, Y. (2013). Predicting Your Next OLAP Query Based on Recent Analytical Sessions. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2013. Lecture Notes in Computer Science, vol 8057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40131-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-40131-2_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40130-5
Online ISBN: 978-3-642-40131-2
eBook Packages: Computer ScienceComputer Science (R0)