BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
OLAP, relational, and multidimensional database systems
ACM SIGMOD Record
Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 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
An array-based algorithm for simultaneous multidimensional aggregates
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
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
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
Fast Computation of Sparse Datacubes
VLDB '97 Proceedings of the 23rd 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
Attribute value reordering for efficient hybrid OLAP
Information Sciences: an International Journal
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ROLAP (Relational OLAP) and MOLAP (Multidimensional OLAP) are two opposing techniques for building On-line Analytical Processing (OLAP) systems. MOLAP has good query performance while ROLAP is based on mature RDBMS technologies. Many data warehouses contain sparse but clustered multidimensional data which neither ROLAP or MOLAP handles effciently and scalably.We propose a dense-region-based OLAP (DROLAP) approach which surpasses both ROLAP and MOLAP in space effciency and query performance. DROLAP takes the bests of ROLAP and MOLAP and combines them to support fast queries and high storage utilization. The core of building a DROLAP system lies in the mining of dense regions in a data cube, for which we have developed an effcient index-based algorithm EDEM to handle. Extensive performance studies consistently show that the DROLAP approach is superior to both MOLAP and ROLAP in handling sparse but clustered multidimensional data. Moreover, our EDEM algorithm is effcient and effective in identifying dense regions.