Quasi-cubes: exploiting approximations in multidimensional databases
ACM SIGMOD Record
Data cube approximation and histograms via wavelets
Proceedings of the seventh international conference on Information and knowledge management
Compressed data cubes for OLAP aggregate query approximation on continuous dimensions
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Explaining Differences in Multidimensional Aggregates
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Mining Multi-Dimensional Constrained Gradients in Data Cubes
Proceedings of the 27th International Conference on Very Large Data Bases
Intelligent Rollups in Multidimensional OLAP Data
Proceedings of the 27th International Conference on Very Large Data Bases
Range CUBE: Efficient Cube Computation by Exploiting Data Correlation
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Diamond in the rough: finding Hierarchical Heavy Hitters in multi-dimensional data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Mining most general multidimensional summarization of probably groups in data warehouses
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A shrinking-based approach for multi-dimensional data analysis
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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The manually performing of the operators turns OLAP analysis a tedious procedure. The huge user's exploration space is the major reason of this problem. Most methods in the literature are proposed in the data perspective, without considering much of the users' interests. In this paper, we adapt the OLAP analysis to the user's interest on the data through the virtual cubes to reduce the user's exploration space in OLAP. We first extract the user's interest from the access history, and then we create the virtual cube accordingly. The virtual cube allows the analysts to focus their eyes only on the interesting data, while the uninteresting information is maintained in a generalized form. The Bayesian estimation was employed to model the access history. We presented the definition and the construction algorithm of virtual cubes. We proposed two new OLAP operators, through which the whole data cube can be obtained, and we also prove that no more response delay is incurred by the virtual cubes. Experiments results show the effectiveness and the efficiency of our approach.