Random sampling with a reservoir
ACM Transactions on Mathematical Software (TOMS)
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Wavelet-based histograms for selectivity estimation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Join synopses for approximate query answering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
The Aqua approximate query answering system
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Congressional samples for approximate answering of group-by queries
SIGMOD '00 Proceedings of the 2000 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
An Efficient Approximation Scheme for Data Mining Tasks
Proceedings of the 17th International Conference on Data Engineering
Overcoming Limitations of Sampling for Aggregation Queries
Proceedings of the 17th International Conference on Data Engineering
Histogram-Based Approximation of Set-Valued Query-Answers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Approximate Query Processing Using Wavelets
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
ICICLES: Self-Tuning Samples for Approximate Query Answering
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Practical Skew Handling in Parallel Joins
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Dynamic sample selection for approximate query processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Effective use of block-level sampling in statistics estimation
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
CORDS: automatic discovery of correlations and soft functional dependencies
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Sampling algorithms in a stream operator
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Derby/S: a DBMS for sample-based query answering
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
BHUNT: automatic discovery of Fuzzy algebraic constraints in relational data
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Robust estimation with sampling and approximate pre-aggregation
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Designing Random Sample Synopses with Outliers
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A sample advisor for approximate query processing
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Metrics for approximate query engine evaluation
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Optimizing Sample Design for Approximate Query Processing
International Journal of Knowledge-Based Organizations
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With the amount of data in current data warehouse databases growing steadily, random sampling is continuously gaining in importance. In particular, interactive analyses of large datasets can greatly benefit from the significantly shorter response times of approximate query processing. Typically, those analytical queries partition the data into groups and aggregate the values within the groups. Further, with the commonly used roll-up and drill-down operations a broad range of group-by queries is posed to the system, which makes the construction of highly-specialized synopses difficult. In this paper, we propose a general-purpose sampling scheme that is biased in order to answer group-by queries with high accuracy. While existing techniques focus on the size of the group when computing its sample size, our technique is based on its standard deviation. The basic idea is that the more homogeneous a group is, the less representatives are required in order to give a good estimate. With an extensive set of experiments, we show that our approach reduces both the estimation error and the construction cost compared to existing techniques.