The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data cube approximation and histograms via wavelets
Proceedings of the seventh international conference on Information and knowledge management
Approximate computation of multidimensional aggregates of sparse data using wavelets
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
Range queries in dynamic OLAP data cubes
Data & Knowledge Engineering
Towards the building of a dense-region-based OLAP system
Data & Knowledge Engineering
Professional Data Warehousing with SQL Server 7.0 and OLAP Services
Professional Data Warehousing with SQL Server 7.0 and OLAP Services
Hierarchical Prefix Cubes for Range-Sum Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Dynamic Update Cube for Range-sum Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
pCube: Update-Efficient Online Aggregation with Progressive Feedback and Error Bounds
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Relative Prefix Sums: An Efficient Approach for Querying Dynamic OLAP Data Cubes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient Online Aggregates in Dense-Region-Based Data Cube Representations
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
International Journal of Wireless and Mobile Computing
Efficient online aggregates in dense-region-based data cube representations
Transactions on large-scale data- and knowledge-centered systems II
Efficient online aggregates in dense-region-based data cube representations
Transactions on large-scale data- and knowledge-centered systems II
International Journal of Intelligent Information and Database Systems
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Data cubes support a powerful data analysis method called the range-sum query. The range-sum query is widely used in finding trends and in discovering relationships among attributes in diverse database applications. A range-sum query computes aggregate information over an online analytical processing (OLAP) data cube in specified query ranges. Existing techniques for range-sum queries on data cubes use an additional cube called the prefix sum cube (PC), to store the cumulative sums of data, causing a high space overhead. This space overhead not only leads to extra costs for storage devices, but also causes additional propagations of updates and longer access time on physical devices.In this paper, we present a new cube representation called 'the PC Pool', which drastically reduces the space of the PC in a large data warehouse. The PC Pool decreases the update propagation caused by the dependency between values in cells of the PC. We develop an effective algorithm, which finds dense sub-cubes from a large data cube. We perform an extensive experiment with diverse data sets, and examine the space reduction and performance of our proposed method with respect to various dimensions of the data cube and query sizes. Experimental results show that our method reduces the space of the PC while having a reasonable query performance.