Indexing and incremental updating condensed data cube

OLAP (online analytical processing) servers usually pre-compute data cubes to improve the response time of possible aggregate queries over cuboids with different grouping attributes. To reduce the huge size of a sparse data cube, the base single tuples (BSTs) are explored to condense cube tuples agg...

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Bibliographic Details
Main Authors: Jianlin Feng, Hongjie Si, Yucai Feng
Format: Conference Proceeding
Language:English
Subjects:
Aggregates
Binary search trees
Computer science
Data analysis
Delay
Indexing
Marketing and sales
Multidimensional systems
Physics computing
Query processing
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Summary:OLAP (online analytical processing) servers usually pre-compute data cubes to improve the response time of possible aggregate queries over cuboids with different grouping attributes. To reduce the huge size of a sparse data cube, the base single tuples (BSTs) are explored to condense cube tuples aggregated from the same set of source tuples into one tuple, whenever such condensing will not require further aggregate when the cube is used to answer queries. We propose the CuboidTree to index the BST condensed cube. Using both synthetic and real world data, we conducted experiments to demonstrate query processing and bulk incremental updating performance of the indexing scheme.
ISSN:1099-3371
DOI:10.1109/SSDM.2003.1214949