A description is given of a Petri-net-based methodology for modeling and evaluation of pipelined processing of concurrent database queries in an integrated data network (IDN). An extended Petri-net model is presented and used to model two key approaches to concurrent database query processing in the IDN, namely, pipelined and data-flow-based execution of queries and intermediate data sharing among concurrent queries. Database operations are categorized, and the models for the data flow and control flow in them are presented. A general-purpose Petri-net simulator has been developed using event-driven programming techniques and used to simulate the execution of the Petri-net models of some test queries. The results validate the results of a previous analytical evaluation in which the advantages of pipeline and intermediate data sharing were established. Since all the essential details of query processing in the IDN have been simulated, the results of this simulation study are believed to present closely the workings of the actual system.>

A query processing strategy which is based on pipelining and data-flow techniques is presented. Timing equations are developed for calculating the performance of four join algorithms: nested block, hash, sort-merge, and pipelined sort-merge. They are used to execute the join operation in a query in distributed fashion and in pipelined fashion. Based on these equations and similar sets of equations developed for other relational algebraic operations, the performance of query execution was evaluated using the different join algorithms. The effects of varying the values of processing time, I/O time, communication time, buffer size, and join selectively on the performance of the pipelined join algorithms are investigated. The results are compared to the results obtained by employing the same algorithms for executing queries using the distributed processing approach which does not exploit the vertical concurrency of the pipelining approach. These results establish the benefits of pipelining.>

Recursive query evaluation is a capability of deductively-augmented database systems that conventional database systems do not support well, if at all. Many recursive queries involve computation of the transitive closure of a relation. Previously published algorithms for transitive closure are iterative in nature, performing repeated joins, unions, and differences until convergence is obtained. In this paper, we present an adaptation of Warren's algorithm for computing the transitive closure of a relation. Warren's algorithm was originally designed for a bit matrix representation of a binary relation; we have adapted it for use with a binary relation represented as a set of tuples, as in a relational database management system. This adapted algorithm computes the transitive closure in two passes over the relation. We analyze the performance of this algorithm, and compare it to the performance of two algorithms based on relational algebra: an iterative algorithm, and an improved version of the iterative algorithm that eliminates unnecessary I/O at the expense of more computation. We evaluate the performance of the algorithms for different source relation sizes, available memory sizes, join selectivities, and maximum path length. Our results show that no algorithm has uniformly superior performance; the adaptation of Warren's algorithm is superior when the source and result relations are not too much larger than main memory. We conclude that in future systems with large main memory, Warren's algorithm generally performs best, and thus should be implemented with the option of switching to an iterative algorithm when the source or result sizes are very large.

The join operation is the most costly operation in relational database management systems. Distributed and parallel processing can effectively speed up the join operation. In this paper, we describe a number of highly parallel and pipelined multiprocessor join algorithms using sort-merge and hashing techniques. Among them, two algorithms are parallel and pipelined versions of traditional sort-merge join methods, two algorithms use both hashing and sort-merge techniques, and another two are variations of the hybrid hash join algorithms. The performance of those algorithms is evaluated analytically against a generic database machine architecture. The methodology used in the design and evaluation of these algorithms is also discussed.

The future data systems are likely to be integrated data networks (IDNs) consisting of a mix of general-purpose computer systems and special-purpose functional processors that are produced by different vendors and have quite different computational power and database management capabilities. Data stored in these networks need to be integrated and shared by the network users. It is important to have a query processing strategy in this type of network that can take advantage of the diverse functional capabilities of the component systems and the parallel processing potential of the network. In this paper, we present a query processing strategy which combines three known techniques: 1) query decomposition, 2) pipelined and data-flow processing of queries, and 3) intermediate result sharing among concurrent queries. The strategies for controlling and managing query and data pipelines are presented. Algorithms for the pipelined execution of the relational join operation are also described. Selected results of the evaluation of the query processing strategy, pipeline control strategies, and parallel algorithms are presented.