Abstract
Interactive analysis of datacube, in which a user navigates a cube by launching a sequence of queries is often tedious since the user may have no idea of what the forthcoming query should be in his current analysis. To better support this process we propose in this paper to apply a Collaborative Work approach that leverages former explorations of the cube to recommend OLAP queries. The system that we have developed adapts Approximate String Matching, a technique popular in Information Retrieval, to match the current analysis with the former explorations and help suggesting a query to the user. Our approach has been implemented with the open source Mondrian OLAP server to recommend MDX queries and we have carried out some preliminary experiments that show its efficiency for generating effective query recommendations. © 2009 Springer Berlin Heidelberg.
Cite
CITATION STYLE
Giacometti, A., Marcel, P., & Negre, E. (2009). Recommending multidimensional queries. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5691 LNCS, pp. 453–466). https://doi.org/10.1007/978-3-642-03730-6_36
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
