Graph-Based Parallel Query Processingand Optimization Strategies for Object-Oriented Databases

  • Authors:

  • Stanley Y. W. Su;Ying Huang;Naoki Akaboshi

  • Affiliations:

  • Database Systems Research and Development Center, Department of Computer and Information Science and Engineering, Department of Electrical and Computer Engineering, University of Florida, Gainesv ...;Database Systems Research and Development Center, Department of Computer and Information Science and Engineering, Department of Electrical and Computer Engineering, University of Florida, Gainesv ...;Database Systems Research and Development Center, Department of Computer and Information Science and Engineering, Department of Electrical and Computer Engineering, University of Florida, Gainesv ...

  • Venue:
  • Distributed and Parallel Databases
  • Year:
  • 1998

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Abstract

Much work has been accomplished in the past on the subject ofparallel query processing and optimization in parallel relational databasesystems; however, little work on the same subject has been done in parallelobject-oriented database systems. Since the object-oriented view of adatabase and its processing are quite different from those of a relationalsystem, it can be expected that techniques of parallel query processing andoptimization for the latter can be different from the former. In this paper,we present a general framework for parallel object-oriented database systemsand several implemented query processing and optimization strategiestogether with some performance evaluation results. In this work,multiwavefront algorithms are used in query processing to allow a higherdegree of parallelism than the traditional tree-based query processing. Fouroptimization strategies, which are designed specifically for themultiwavefront algorithms and for the optimization of single as well asmultiple queries, are introduced. The query processing algorithms andoptimization strategies have been implemented on a parallel computer,nCUBE2; and the results of a performance evaluation are presented in thispaper. The main emphases and the intended contributions of this paper are(1) data partitioning, query processing and optimization strategiessuitable for parallel OODBMSs, (2) the implementation of the multiwavefrontalgorithms and optimization strategies, and (3) the performance evaluationresults.