A linear-time probabilistic counting algorithm for database applications

A linear-time probabilistic counting algorithm for database applications (1990)

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by Kyu-young Whang , Brad T. Vander-zanden , Howard M. Taylor

Venue:	ACM Transactions on Database Systems
Citations:	74 - 5 self

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Datum	Value	Source
TITLE	A linear-time probabilistic counting algorithm for database applications	INFERENCE
AUTHOR NAME	Kyu-young Whang	SVM HeaderParse 0.2
AUTHOR AFFIL	Korea Advanced Institute of Science and Technology	SVM HeaderParse 0.2
AUTHOR NAME	Brad T. Vander-zanden	SVM HeaderParse 0.2
AUTHOR AFFIL	Cornell University; and	SVM HeaderParse 0.2
AUTHOR NAME	Howard M. Taylor	SVM HeaderParse 0.2
AUTHOR AFFIL	; University of Delaware; Authors ’ addresses: K.-Y. Whang	SVM HeaderParse 0.2
AUTHOR ADDR	; Science and Technology, P.O. Box 150, Cheong-Ryang ui, Seoul, Korea; B. T. Vander-Zanden,	SVM HeaderParse 0.2
ABSTRACT	We present a probabilistic algorithm for counting the number of unique values in the presence of duplicates. This algorithm has O(q) time complexity, where q is the number of values including duplicates, and produces an estimation with an arbitrary accuracy prespecified by the user using only a small amount of space. Traditionally, accurate counts of unique values were obtained by sorting, which has O(q log q) time complexity. Our technique, called linear counting, is based on hashing. We present a comprehensive theoretical and experimental analysis of linear counting. The analysis reveals an interesting result: A load factor (number of unique values/hash table size) much larger than 1.0 (e.g., 12) can be used for accurate estimation (e.g., 1 % of error). We present this technique with two important applications to database problems: namely, (1) obtaining the column cardinality (the number of unique values in a column of a relation) and (2) obtaining the join selectivity (the number of unique values in the join column resulting from an unconditional join divided by the number of unique join column values in the relation to he joined). These two parameters are important statistics that are used in relational query optimization and physical database design.	SVM HeaderParse 0.2
YEAR	1990	INFERENCE
VENUE	ACM Transactions on Database Systems	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	208--229	INFERENCE
VOLUME	15	INFERENCE
CITATIONS	19 found	ParsCit 1.0