Towards OLAP query reformulation in peer-to-peer data warehousing
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
New Frontiers in business intelligence: distribution and personalization
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
A survey on representation, composition and application of preferences in database systems
ACM Transactions on Database Systems (TODS)
Mining preferences from OLAP query logs for proactive personalization
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
OLAP query reformulation in peer-to-peer data warehousing
Information Systems
Semantics and usage statistics for multi-dimensional query expansion
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Towards intensional answers to OLAP queries for analytical sessions
Proceedings of the fifteenth international workshop on Data warehousing and OLAP
Multi-dimensional navigation modeling using BI analysis graphs
ER'12 Proceedings of the 2012 international conference on Advances in Conceptual Modeling
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Multidimensional databases are the core of business intelligence systems. Their users express complex OLAP queries, often returning large volumes of facts, sometimes providing little or no information. Thus, expressing preferences could be highly valuable in this domain. The OLAP domain is representative of an unexplored class of preference queries, characterized by three peculiarities: preferences can be expressed on both numerical and categorical domains; they can also be expressed on the aggregation level of facts; the space on which preferences are expressed includes both elemental and aggregated facts. In this paper, we present myOLAP, an approach for expressing and evaluating OLAP preferences, devised by taking into account the three peculiarities above. We first propose a preference algebra where users are enabled to express their preferences, besides on attributes and measures, also on the aggregation level of facts, for instance, by stating that monthly data are preferred to yearly and daily data. Then, with respect to preference evaluation, we propose an algorithm called WeSt that relies on a novel graph representation where two types of domination between sets of facts may be expressed, which considerably improves efficiency. The approach is extensively tested for efficiency and effectiveness on real data, and compared against two other approaches in the literature.