Sonia Cafieri, Jon Lee, et al.
Journal of Global Optimization
In data warehousing and OLAP applications, scalar-level predicates in SQL become increasingly inadequate to support a class of operations that require set-level comparison semantics, i.e., comparing a group of tuples with multiple values. Currently, complex SQL queries composed by scalar-level operations are often formed to obtain even very simple set-level semantics. Such queries are not only difficult to write but also challenging for a database engine to optimize, thus can result in costly evaluation. This paper proposes to augment SQL with set predicate, to bring out otherwise obscured set-level semantics. We studied two approaches to processing set predicates - an aggregate function-based approach and a bitmap index-based approach. Moreover, we designed a histogram-based probabilistic method of set predicate selectivity estimation, for optimizing queries with multiple predicates. The experiments verified its accuracy and effectiveness in optimizing queries. © 1989-2012 IEEE.
Sonia Cafieri, Jon Lee, et al.
Journal of Global Optimization
Elliot Linzer, M. Vetterli
Computing
Sai Zeng, Angran Xiao, et al.
CAD Computer Aided Design
B.K. Boguraev, Mary S. Neff
HICSS 2000