Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP
Iceberg-cube computation with PC clusters
SIGMOD '01 Proceedings of the 2001 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
Intelligent Rollups in Multidimensional OLAP Data
Proceedings of the 27th International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Supporting Dimension Updates in an OLAP Server
CAiSE '02 Proceedings of the 14th International Conference on Advanced Information Systems Engineering
Materializing views with minimal size to answer queries
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
QC-trees: an efficient summary structure for semantic OLAP
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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 one-pass aggregation algorithm with the optimal buffer size in multidimensional OLAP
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
High-dimensional OLAP: a minimal cubing approach
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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OLAP provides an efficient way for business data analysis. However, most up-to-date OLAP tools often make the analysts lost in the sea of data while the analysts usually focus their interest on a subset of the whole dataset. Unfortunately, OLAP operators are usually not capsulated within the subset. What's more, the users' interests often arise in an impromptu way after the user getting some information from the data. In this paper, we give the definition of users' interests and propose the user-defined virtual cubes to solve this problem. At the same time, we present an algorithm to answer the queries upon virtual cube. All the OLAP operators will be encapsulated within this virtual cube without superfluous information retrieved. Experiments show the effectiveness and efficiency of the virtual cube mechanism.