Efficient methods of processing unanticipated queries are a crucial prerequisite for the success of generalized database management systems. A wide variety of approaches to improve the performance of query evaluation algorithms have been proposed: logic-based and semantic transformations, fast implementations of basic operations, and combinatorial or heuristic algorithms for generating alternative access plans and choosing among them. These methods are presented in the framework of a general query evaluation procedure using the relational calculus representation of queries. In addition, nonstandard query optimization issues such as higher level query evaluation, query optimization in distributed databases, and use of database machines are addressed. The focus, however, is on query optimization in centralized database systems.

This paper addresses the vertical partitioning of a set of logical records or a relation into fragments. The rationale behind vertical partitioning is to produce fragments, groups of attribute columns, that “closely match ” the requirements of transactions. Vertical partitioning is applied in three contexts: a database stored on devices of a single type, a database stored in different memory levels, and a distributed database. In a two-level memory hierarchy, most transactions should be processed using the fragments in primary memory. In distributed databases, fragment allocation should maximize the amount of local transaction process-ing. Fragments may be nonoverlapping or overlapping. A two-phase approach for the determination of fragments is proposed; in the first phase, the design is driven by empirical objective functions which do not require specific cost information. The second phase performs cost optimization by incorporating the knowledge of a specific application environment. The algorithms presented in this paper have been implemented, and examples of their actual use are shown. 1.

This work addresses a files allocation problem (FAP) in distributed computing systems. This FAP attempts to minimize the expected data transfer time for a specific program that must access several data files from non-perfect computer sites. We assume that communication capacity can be reserved; hence, the data transmission behavior is modeled as a many-to-one multi-commodity flow problem. A new critical-cut method is proposed to solve this reduced multi-commodity flow problem. Based on this method, two algorithms which use branch-and-bound are proposed for this FAP. The proposed algorithms are able to allocate data files having single copies or multiple replicated copies. Simulation results are presented to demonstrate the performance of the algorithms.