Random sampling with a reservoir
ACM Transactions on Mathematical Software (TOMS)
Elements of statistical computing
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Randomized algorithms
Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Quasi-cubes: exploiting approximations in multidimensional databases
ACM SIGMOD Record
AutoAdmin “what-if” index analysis utility
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An application of mathematical programming to sample allocation
Computational Statistics & Data Analysis
Data cube approximation and histograms via wavelets
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Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
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Join synopses for approximate query answering
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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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Congressional samples for approximate answering of group-by queries
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Selectivity estimation using probabilistic models
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Machine Learning
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Overcoming Limitations of Sampling for Aggregation Queries
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VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
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ICICLES: Self-Tuning Samples for Approximate Query Answering
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The VLDB Journal — The International Journal on Very Large Data Bases
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Turbo-charging hidden database samplers with overflowing queries and skew reduction
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Online stratified sampling: evaluating classifiers at web-scale
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Stratified reservoir sampling over heterogeneous data streams
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
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RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Effective and efficient sampling methods for deep web aggregation queries
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The VC-dimension of SQL queries and selectivity estimation through sampling
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Effective stratification for low selectivity queries on deep web data sources
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Self-adaptive approximate queries for large-scale information aggregation
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Adaptive stratified reservoir sampling over heterogeneous data streams
Information Systems
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The ability to approximately answer aggregation queries accurately and efficiently is of great benefit for decision support and data mining tools. In contrast to previous sampling-based studies, we treat the problem as an optimization problem where, given a workload of queries, we select a stratified random sample of the original data such that the error in answering the workload queries using the sample is minimized. A key novelty of our approach is that we can tailor the choice of samples to be robust, even for workloads that are “similar” but not necessarily identical to the given workload. Finally, our techniques recognize the importance of taking into account the variance in the data distribution in a principled manner. We show how our solution can be implemented on a database system, and present results of extensive experiments on Microsoft SQL Server that demonstrate the superior quality of our method compared to previous work.