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Join Synopses for Approximate Query Answering by Acharya, Swarup; Gibbons, Phillip B.; Poosala, Viswanath; Ramaswamy, Sridhar is a Computer Science article available to read on EtoBox.

What is Join Synopses for Approximate Query Answering about?

In large data warehousing environments, it is often advantageous to provide fast, approximate answers to complex aggregate queries based on statistical summaries of the full data. In this paper, we demonstrate the difficulty of providing good approximate answers for join-queries using only statistics (in particular, samples) from the base relations. We propose __join synopses__ as an effective solution for this problem and show how precomputing just __one join synopsis__ for each relation suffices to significantly improve the quality of approximate answers for arbitrary queries with foreign key joins. We present optimal strategies for allocating the available space among the various join synopses when the query work load is known and identify heuristics for the common case when the work load is not known. We also present efficient algorithms for incrementally maintaining join synopses in the presence of updates to the base relations. Our extensive set of experiments on the TPC-D benchmark database show the effectiveness of join synopses and various other techniques proposed in this paper.

Who reads Join Synopses for Approximate Query Answering?

Author

Acharya, Swarup; Gibbons, Phillip B.; Poosala, Viswanath; Ramaswamy, Sridhar

Publisher

Association for Computing Machinery; Association for Computing Machinery (ACM) (ISSN 0163-5808)

Published

1999

Language

EN

Field

Computer Science (Physical Sciences)