Quickly generating billion-record synthetic databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Cubetree: organization of and bulk incremental updates on the data cube
SIGMOD '97 Proceedings of the 1997 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 2002 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
QC-trees: an efficient summary structure for semantic OLAP
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Condensed Cube: An Efficient Approach to Reducing Data Cube Size
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Indexing and incremental updating condensed data cube
SSDBM '03 Proceedings of the 15th 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
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Research in data warehouse modeling and design: dead or alive?
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
FCLOS: A client-server architecture for mobile OLAP
Data & Knowledge Engineering
A Multiple Correspondence Analysis to Organize Data Cubes
Proceedings of the 2007 conference on Databases and Information Systems IV: Selected Papers from the Seventh International Baltic Conference DB&IS'2006
MOLAP cube based on parallel scan algorithm
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
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BST Condensed Cube is a fully computed cube that condenses those tuples, which are aggregated from the same single base relation tuple, into one physical tuple. Although it has been proved to be an effective approach to reduce the size of a data cube, there still exist some redundancies in a BST condensed cube, i.e., prefix redundancy among cube tuples. In this paper, we augument BST condensing with prefix-sharing, and propose an efficient cube structure called PrefixCube, for further reducing a BST condensed data cube's size as well as its computation time. The space and time savings of PrefixCube, compared with its corresponding BST condensed cube, are demonstrated through extensive experiments, using both synthetic and real world data.