Implementation and evaluation of parallel query processing algorithms and data partitioning heuristics in object-oriented databases

Object-oriented database management systems (OODBMSs) provide rich facilities for the modeling and processing of structural as well as behavioral properties of complex application objects. However, due to their inherent generality and continuously evolving functionalities, efficient implementations are important for these OODBMSs to support the present and future applications, particularly when the databases are very large. In this paper, we present several parallel, multi-wavefront algorithms based on two processing approaches, i.e., identification and elimination approaches, to verify association patterns specified in queries. Both approaches allow more processors to operate concurrently on a query than the traditional tree-structured query processing approach, thus introducing a higher degree of parallelism in query processing. A heuristic method is presented for partitioning an object-oriented database (OODB). The main consideration for partitioning the database is load balancing. This method also tries to reduce the communication time by reducing the length of the path that wavefronts need to be propagated. Multiple wavefront algorithms based on the two approaches for tree-structured queries have been implemented on an nCUBE 2 parallel computer. The implementation of the query processor allows multiple queries to be executed simultaneously. This implementation provides an environment for evaluating the algorithms and the heuristic method for partitioning the database. The evaluation results are presented in this paper.Recommended by: Patrick Valduriez