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Pessimistic Cardinality Estimation
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Cardinality Estimation is to estimate the size of the output of a query without computing it, by using only statistics on the input relations. Existing estimators try to return an unbiased estimate of the cardinality: this is notoriously difficult. A new class of estimators have been proposed recently, called "pessimistic estimators", which compute a guaranteed upper bound on the query output. Two recent advances have made pessimistic estimators practical. The first is the recent observation that degree sequences of the input relations can be used to compute query upper bounds. The second is a long line of theoretical results that have developed the use of information theoretic inequalities for query upper bounds. This paper is a short overview of pessimistic cardinality estimators, contrasting them with traditional estimators.
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Cited by 1 Pith paper
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Quantum Information-Theoretical Size Bounds for Conjunctive Queries with Functional Dependencies
Worst-case conjunctive query size bounds can be reformulated with quantum Rényi entropy, producing sound but generally non-tight upper bounds whose classical tight version is recovered only in the α→1 limit.
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