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...

• Type: Conference
• Year: 2005
• Where: VLDB
• Authors: Liang Jin, Chen Li
• Keywords: Database, Fuzzy String Predicates, Global Histogram, Histogram Structures, VLDB 2005