Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of object-oriented and extensible database systems will not solve this problem. On the contrary, modern data models exacerbate it: In order to manipulate large sets of complex objects as efficiently as today’s database systems manipulate simple records, query processing algorithms and software will become more complex, and a solid understanding of algorithm and architectural issues is essential for the designer of database management software. This survey provides a foundation for the design and implementation of query execution facilities in new database management systems. It describes a wide array of practical query evaluation techniques for both relational and post-relational database systems, including iterative execution of complex query evaluation plans, the duality of sort- and hash-based set matching algorithms, types of parallel query execution and their implementation, and special operators for emerging database application domains.

ABSTRACT: In a high level query and data manipulation language such as SQL, requests are stated non-procedurally, without reference to access paths. This paper describes how System R chooses access paths for both simple (single relation) and complex queries (such as joins), given a user specification of desired data as a boolean expression of predicates. System R is an experimental database management system developed to carry out research on the relational model of data. System R was designed and built by members of the IBM San Jose Research'Laboratory. 1.

The evolution of data base technology over the past twenty-five years is surveyed, and major IBM contributions to this technology are identified and briefly described.

Runtime Reorganization of Parallel and Distributed Expert Database Systems Hasanat M. Dewan In the last decade and half, scientific and commercial databases have grown at a fast pace as increasingly many enterprises have begun offering and using databasecentered services. Concomitantly, new application requirements have placed unusual processing demands on existing databases. Many researchers believe that next generation database systems will be required to provide efficient and scalable facilities for high level inference and complex query processing. In this thesis, we address the efficiency and scalability problem in the context of Database Rule Processing Systems (DRPS). We propose a scalable strategy for the efficient parallel processing of rule programs applied to massive databases. We introduce predictive load balancing techniques in a synchronous model of rule execution, where the variance in runtime of the distributed sites is minimized per cycle of rule processing, thus incr...

California. It is the Association's foremost award for technical contributions to the computing community. Codd was selected by the ACM General Technical Achievement Award Committee for his "~undamental and continuing contributions to the theory and practice of database management systems. " The originator of the relational model for databases, Codd has made further important contribu-tions in the development of relational algebra, relational calculus, and normalization of relations. Edgar E Coddjoined IBM in 1949 to prepare programs for the Selective Sequence Electronic Calculator. Since then, his work in computing has encompassed logical design of computers [IBM 701 and Stretch], managing a computer center in Canada, heading the development of one of the first operating systems with a general multiprogramming capability, con-

Throughout the history of information storage in computers, one of the most readily observable trends has been the focus on data independence.

... processing today involves the taking of counts, sums, averages, and other sta-tistical or aggregate quantities. The "Abe” query language is designed to make formulation of complicated aggregations simple. Access path selection in Abe finds efficient ways to execute these complicated queries. Access paths for Abe queries perform “aggregate joins”, that is, they compute aggregate quantities at the same time as they join subqueries with parent queries. This can be done using index scans or merging scans depending on how many “partitions” need to be accessed.