Optimal histograms for limiting worst-case error propagation in the size of join results
ACM Transactions on Database Systems (TODS)
Adaptive selectivity estimation using query feedback
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Balancing histogram optimality and practicality for query result size estimation
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Histogram-based estimation techniques in database systems
Histogram-based estimation techniques in database systems
Self-tuning histograms: building histograms without looking at data
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
Locally adaptive dimensionality reduction for indexing large time series databases
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
Fast, small-space algorithms for approximate histogram maintenance
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
RHist: adaptive summarization over continuous data streams
Proceedings of the eleventh international conference on Information and knowledge management
Optimal Histograms with Quality Guarantees
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Combining Histograms and Parametric Curve Fitting for Feedback-Driven Query Result-size Estimation
VLDB '99 Proceedings of the 25th 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
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
Histogramming Data Streams with Fast Per-Item Processing
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Opportunistic data structures with applications
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Approximating a Data Stream for Querying and Estimation: Algorithms and Performance Evaluation
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
The history of histograms (abridged)
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Data streams: algorithms and applications
Foundations and Trends® in Theoretical Computer Science
Hierarchical synopses with optimal error guarantees
ACM Transactions on Database Systems (TODS)
Multiplicative synopses for relative-error metrics
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Consistent histograms in the presence of distinct value counts
Proceedings of the VLDB Endowment
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Foundations and Trends in Databases
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A histogram is a piecewise-constant approximation of an observed data distribution. A histogram is used as a small-space, approximate synopsis of the underlying data distribution, which is often too large to be stored precisely. Histograms have found many applications in database management systems, perhaps most commonly for query selectivity estimation in query optimizers [1], but have also found applications in approximate query answering [2], load balancing in parallel join execution [3], mining time-series data [4], partition-based temporal join execution, query pro.ling for user feedback, etc. Ioannidis has a nice overview of the history of histograms, their applications, and their use in commercial DBMSs [5]. Also, Poosala’s thesis provides a systematic treatment of different types of histograms [3].