Paper Info
Title
Discovering and Exploiting Statistical Properties for Query Optimization in Relational Databases: A Survey
Abstract
Discovering and exploiting statistical features in relational datasets is key to query optimization in a relational database management system (RDBMS ), and is also needed for database design, cleaning, and integration. This paper surveys a variety of methods for automatically discovering important statistical features such as correlations, functional dependencies, keys, and algebraic constraints. We discuss proactive approaches in which the data is scanned or sampled (periodically, at optimization time or at query time), or in which exploratory queries are executed. Also discussed are reactive approaches that monitor the results of the query processing. Finally, we discuss methods for dealing with the practical challenges of maintaining statistical information in the face of heavy system utilization, and of dealing with inconsistencies that arise from incomplete cardinality models, use of multiple discovery methods, or changes in the underlying data over time. © 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 1: 000-000, 2008
Year
DOI
Venue
2009
10.1002/sam.v1:4
Statistical Analysis and Data Mining
Keywords
Field
DocType
important statistical feature,exploratory query,relational databases,relational database management system,query optimization,exploiting statistical properties,statistical information,query time,statistical feature,query processing,heavy system utilization,optimization time,database design,sampling,data mining,relational database
Query optimization,Data mining,Conjunctive query,Query language,Information retrieval,Relational database,Computer science,Sargable,Web query classification,View,Query by Example
Journal
Volume
Issue
Citations 
1
4
8
PageRank 
References 
Authors
0.45
5
4
Name
Order
Citations
PageRank
Peter J. Haas12799454.10
Ihab F. Ilyas22907117.27
Guy M. Lohman32846965.94
Volker Markl42245182.37