Abstract
As the amount and complexity of data keep increasing in data warehouses, their exploration for analytical purposes may be hindered. Recommender systems have grown very popular on the Web with sites like Amazon, Netflix, etc. These systems proved successful to help users explore available content related to what they are currently looking at. Recent systems consider the use of recommendation techniques to suggest data warehouse queries and help an analyst pursue its exploration. In this paper, we present a personalized query expansion component which suggests measures and dimensions to iteratively build consistent queries over a data warehouse. Our approach leverages (a) semantics defined in multi-dimensional domain models, (b) collaborative usage statistics derived from existing repositories of Business Intelligence documents like dashboards and reports and (c) preferences defined in a user profile. We finally present results obtained with a prototype implementation of an interactive query designer.
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Thollot, R., Kuchmann-Beauger, N., Aufaure, MA. (2012). Semantics and Usage Statistics for Multi-dimensional Query Expansion. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29035-0_19
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DOI: https://doi.org/10.1007/978-3-642-29035-0_19
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