Recursive estimation and time-series analysis: an introduction
Recursive estimation and time-series analysis: an introduction
Adaptive selectivity estimation using query feedback
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Self-tuning histograms: building histograms without looking at data
SIGMOD '99 Proceedings of the 1999 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
Best-effort cache synchronization with source cooperation
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Fast incremental maintenance of approximate histograms
ACM Transactions on Database Systems (TODS)
Selectivity Estimation Without the Attribute Value Independence Assumption
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Average-Case Competitive Analyses for Ski-Rental Problems
ISAAC '02 Proceedings of the 13th International Symposium on Algorithms and Computation
Bypass Caching: Making Scientific Databases Good Network Citizens
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
ISOMER: Consistent Histogram Construction Using Query Feedback
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Estimating query result sizes for proxy caching in scientific database federations
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
On Transposing Large 2nx 2nMatrices
IEEE Transactions on Computers
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Recently several database-based applications have emerged that are remote from data sources and need accurate histograms for query cardinality estimation. Traditional approaches for constructing histograms require complete access to data and are I/O and network intensive, and therefore no longer apply to these applications. Recent approaches use queries and their feedback to construct and maintain "workload aware" histograms. However, these approaches either employ heuristics, thereby providing no guarantees on the overall histogram accuracy, or rely on detailed query feedbacks, thus making them too expensive to use. In this paper, we propose a novel, incremental method for constructing histograms that uses minimum feedback and guarantees minimum overall residual error. Experiments on real, high dimensional data shows 30-40% higher estimation accuracy over currently known heuristic approaches, which translates to significant performance improvement of remote applications.