Multi-table joins through bitmapped join indices
ACM SIGMOD Record
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Requirement-based data cube schema design
Proceedings of the eighth international conference on Information and knowledge management
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
Materialized View Selection for Multidimensional Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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In an OLAP system, we can use data cubes (precomputed multidimensional views of data) to support real-time queries. To reduce the maintenance cost, which is related to the number of cubes materialized, some cubes can be merged, but the resulting larger cubes will increase the response time of answering some queries. In order to satisfy the maintenance bound and response time bound given by the user, we may have to sacrifice some of the queries and not to take them into our consideration. The optimization problem in the data cube system design is to optimize an initial set of cubes such that the system can answer a maximum number of queries and satisfy the bounds. This is an NP-complete problem. Approximate algorithms Greedy Removing and 2-Greedy Merging are proposed. Experiments have been done on a census database and the results show that our approach is both effective and efficient.