Recommending Multidimensional Queries
Giacometti, Arnaud; Marcel, Patrick; Negre, Elsa (2009), Recommending Multidimensional Queries, in Pedersen, Torben B; Mohania, Mukesh K; Tjoa, A Min, Data Warehousing and Knowledge Discovery 11th International Conference, DaWaK 2009 Linz, Austria, August 31–September 2, 2009 Proceedings, Springer : Berlin, p. 453-466. http://dx.doi.org/10.1007/978-3-642-03730-6_36
TypeCommunication / Conférence
Conference title11th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2009)
Book titleData Warehousing and Knowledge Discovery 11th International Conference, DaWaK 2009 Linz, Austria, August 31–September 2, 2009 Proceedings
Book authorPedersen, Torben B; Mohania, Mukesh K; Tjoa, A Min
Series titleLecture Notes in Computer Science
Number of pages480
MetadataShow full item record
Abstract (EN)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.
Subjects / KeywordsOLAP queries; Interactive analysis of datacube
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