Near optimal multiple choice index selection for relational databases

AbstractIndex selection for relational databases is an important issue which has been researched quite extensively [1–5]. In the literature, in index selection algorithms for relational databases, at most one index is considered as a candidate for each attribute of a relation. However, it is possible that more than one different type of indexes with different storage space requirements may be present as candidates for an attribute. Also, it may not be possible to eliminate locally all but one of the candidate indexes for an attribute due to different benefits and storage space requirements associated with the candidates. Thus, the algorithms available in the literature for optimal index selection may not be used when there are multiple candidates for each attribute and there is a need for a global optimization algorithm in which at most one index can be selected from a set of candidate indexes for an attribute. The problem of index selection in the presence of multiple candidate indexes for each attribute (which we call the multiple choice index selection problem) has not been addressed in the literature. In this paper, we present the multiple choice index selection problem, show that it is NP-hard, and present an algorithm which gives an approximately optimal solution within a user specified error bound in a logarithmic time order

North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2

This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies