An array-based algorithm for simultaneous multidimensional aggregates
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
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
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
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Dwarfs in the rearview mirror: how big are they really?
Proceedings of the VLDB Endowment
Expectation propagation in genspace graphs for summarization
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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QC-Trees is one of the most storage-efficient structures for data cubes in a MOLAP system. Although QC-Trees can achieve a high compression ratio, it is still a fully materialized data cube. In this paper, we present an improved structure PMC, which allow us to partially materialize cells in a QC-Trees. There is a sharp contrast between our partially materialization algorithm and other extensively studied materialized view selection algorithms. If a view is selected in a traditional algorithm, then all cells in this selected view are to be materialized. Our algorithm, however, selects and materializes data by cells. Experiments results show that PMC can further reduce storage space occupied by the data cube, and can shorten the time for update the cube. Along with further reduced space and update cost, our algorithm can ensure a stable query performance.