Sciweavers

Share
VLDB
2005
ACM

Selectivity Estimation for Fuzzy String Predicates in Large Data Sets

12 years 5 months ago
Selectivity Estimation for Fuzzy String Predicates in Large Data Sets
Many database applications have the emerging need to support fuzzy queries that ask for strings that are similar to a given string, such as “name similar to smith” and “telephone number similar to 412-0964.” Query optimization needs the selectivity of such a fuzzy predicate, i.e., the fraction of records in the database that satisfy the condition. In this paper, we study the problem of estimating selectivities of fuzzy string predicates. We develop a novel technique, called Sepia, to solve the problem. It groups strings into clusters, builds a histogram structure for each cluster, and constructs a global histogram for the database. It is based on the following intuition: given a query string q, a preselected string p in a cluster, and a string s in the cluster, based on the proximity between q and p, and the proximity between p and s, we can obtain a probability distribution from a global histogram about the similarity between q and s. We give a full specification of the tech...
Liang Jin, Chen Li
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where VLDB
Authors Liang Jin, Chen Li
Comments (0)
books