SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Personalizing queries based on networks of composite preferences
ACM Transactions on Database Systems (TODS)
Efficient and scalable method for processing top-k spatial Boolean queries
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
New Frontiers in business intelligence: distribution and personalization
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Multidimensional cyclic graph approach: Representing a data cube without common sub-graphs
Information Sciences: an International Journal
Being picky: processing top-k queries with set-defined selections
Proceedings of the 21st ACM international conference on Information and knowledge management
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Many new applications that involve decision making need online (i.e., OLAP-styled) preference analysis with multi-dimensional boolean selections. Typical preference queries includes top-k queries and skyline queries. An analytical query often comes with a set of boolean predicates that constrain a target subset of data, which, may also vary incrementally by drilling/rolling operators. To efficiently support preference queries with multiple boolean predicates, neither boolean-then-preference nor preference-then-boolean approach is satisfactory. To integrate boolean pruning and preference pruning in a unified framework, we propose signature, a new materialization measure for multi-dimensional group-bys. Based on this, we propose P-Cube (i.e., data cube for preference queries) and study its complete life cycle, including signature generation, compression, decomposition, incremental maintenance and usage for efficient on-line analytical query processing. We present a signature-based progressive algorithm that is able to simultaneously push boolean and preference constraints deep into the database search. Our performance study shows that the proposed method achieves at least one order of magnitude speed-up over existing approaches.