Approximating multi-dimensional aggregate range queries over real attributes
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
STHoles: a multidimensional workload-aware histogram
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
A robust, optimization-based approach for approximate answering of aggregate queries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
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TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
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DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
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The VLDB Journal — The International Journal on Very Large Data Bases
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Handbook of massive data sets
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Selectivity estimators for multidimensional range queries over real attributes
The VLDB Journal — The International Journal on Very Large Data Bases
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Data & Knowledge Engineering
Pre-aggregation with probability distributions
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
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Journal of Intelligent Information Systems
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ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
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International Journal of Intelligent Information and Database Systems
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Modern decision support systems require very quick (interactive) responses from the DBMS, but pose complex queries on large volumes of data. In this paper, we present a novel solution to this problem: we precompute concise histogram statistics on the data to answer the queries quickly, but approximately. Our hypothesis is that many decision support applications can tolerate small errors in query results in return for large reductions in response times.In particular, we propose the use of multiple histograms to approximate the data cube and answer aggregate queries approximately using this summarized data. We enhance histograms to estimate the quality of the approximate answers. We primarily explore the interaction among various histograms on the data cube in order to minimize the space needed when an upper bound on the errors is given. Our main contribution in this paper is an efficient technique for selecting a provably near-optimal set of histograms on the data cube. Extensive experiments show that our technique results in very accurate and concise statistics.