Workload-Optimal Histograms on Streams | Zendy

S. Muthukrishnan | Zendy; Michael Strauss | Zendy; Xue Zheng | Zendy

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Workload-Optimal Histograms on Streams

Author(s) -

S. Muthukrishnan,

Michael Strauss,

Xue Zheng

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-29118-0

DOI - 10.1007/11561071_65

Subject(s) - computer science , histogram , partition (number theory) , query optimization , query plan , data mining , workload , streaming data , piecewise , information retrieval , sargable , artificial intelligence , web search query , search engine , mathematical analysis , mathematics , combinatorics , image (mathematics) , operating system

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].

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