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Performance analysis of parallel object-oriented query processing algorithms

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Abstract

Advanced application domains such as computer-aided design, computer-aided software engineering, and office automation are characterized by their need to store, retrieve, and manage large quantities of data having complex structures. A number of object-oriented database management systems (OODBMS) are currently available that can effectively capture and process the complex data. The existing implementations of OODBMS outperform relational systems by maintaining and querying cross-references among related objects. However, the existing OODBMS still do not meet the efficiency requirements of advanced applications that require the execution of complex queries involving the retrieval of a large number of data objects and relationships among them. Parallel execution can significantly improve the performance of complex OO queries. In this paper, we analyze the performance of parallel OO query processing algorithms for various benchmark application domains. The application domains are characterized by specific mixes of queries of different semantic complexities. The performance of the application domains has been analyzed for various system and data parameters by running parallel programs on a 32-node transputer based parallel machine developed at the IBM Research Center at Yorktown Heights. The parallel processing algorithms, data routing techniques, and query management and control strategies have been implemented to obtain accurate estimation of controlling and processing overheads. However, generation of large complex databases for the study was impractical. Hence, the data used in the simulation have been parameterized. The parallel OO query processing algorithms analyzed in this study are based on a query graph approach rather than the traditional query tree approach. Using the query graph approach, a query is processed by simultaneously initiating the execution at several object classes, thereby, improving the parallelism. During processing, the algorithms avoid the execution of time-consuming join operations by making use of the object references among the objects. Further, the algorithms do not generate any temporary data, thereby, reducing disk accesses. This is accomplished by marking the selected objects and by employing a two-phase query processing strategy.

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References

  1. A.M. Alashqur, “A query model and query and knowledge definition languages for object-oriented databases,” Ph.D. dissertation, Department of Electrical Engineering, University of Florida, 1989.

  2. A.M. Alashqur, S.Y.W. Su, and H. Lam, “OQL: a query language for manipulating object-oriented databases,” inProc. Int. Conf. VLDB, Amsterdam, Netherlands, 1989, pp. 433–442.

  3. “Alternate workstation server architectures for object-oriented database systems,” inProc. 16th Int. Conf. VLDB, Brisbane, Australia, 1990, pp. 107–121.

  4. F. Bancilhon, S. Cluet , and C. Delobel, “A query language for O2,” inBuilding an object-oriented database system—The Story of O2, F. Bancilhon, C. Delobel and P. Kanellakis (Eds.), Morgan Kaufmann, 1992, pp. 234–255.

  5. C.K. Baru and O. Frieder, “Database operations in a cube-connected multicomputer system,”IEEE trans. Compu., vol. C-38, pp. 920–927, 1989.

    Google Scholar 

  6. C.K. Baru and S. Padmanabhan, “Join and data redistribution algorithms for hypercubes,”IEEE Trans. Knowledge Data Eng., vol. 5, no. 1, pp. 161–168, 1993.

    Google Scholar 

  7. C.K. Baru and S.Y.W. Su, “The architecture of SM3: a dynamically partitionable multicomputer system,”IEEE Trans. Compu., vol. C-35, no. 9, pp. 780–802, 1986.

    Google Scholar 

  8. D. Batory and W. Kim, “Modeling concepts for VLSI CAD objects,”ACM Trans. on Database Systems, vol. 10, no. 3, pp. 322–346, 1985.

    Google Scholar 

  9. E. Bertino, M. Negri, G. Pelagatti, and L. Sbattella, “Object-oriented query languages: the notion and the issues,”IEEE Trans. Knowledge Data Eng., vol. 4, no. 3, pp. 223–237, 1992.

    Google Scholar 

  10. L. Bic and L.R. Hartmann, “Simulated performance of a data-driven database machine,”J. Parallel Distrib. Comput. vol. 3, no. 1, pp. 1–22, 1986.

    Google Scholar 

  11. L. Bic and L.R. Hartmann, “AGM: A dataflow database machine,”ACM Trans. Database Systems, vol. 14, no. 1, pp. 114–146, 1989.

    Google Scholar 

  12. Communications of the ACM, Special Issue on Next-Generation Database Systems, vol. 34, no. 10, 1991.

  13. D.J. DeWitt, “DIRECT-a multiprocessor organization for supporting relational database management systems,”IEEE Trans. Comput. vol. C-28, pp. 395–406, 1979.

    Google Scholar 

  14. M. Muralikrishna, “GAMMA: a performance dataflow database machine,” inProc. Twelfth Int. Conf. Very Large Databases, Kyoto, Japan, pp. 228–237, 1986.

  15. R.H. Gerber, “Dataflow query processing using multiprocessor hash-partitioned algorithms,” Ph.D. dissertation, University of Wisconsin, Winsconsin, 1986.

    Google Scholar 

  16. S.E. Hudson and R. King, “A self-adaptive, concurrent implementation of an object-oriented database management system,”ACM Trans. Database Systems, vol. 14, no. 3, pp. 291–321, 1989.

    Google Scholar 

  17. R. Hull and R. King, “Semantic database modeling: survey, applications, and research issues,”ACM Comput. Surveys, vol. 19, no. 3, pp. 201–260, 1987.

    Google Scholar 

  18. W. Kim, “A model of queries for object-oriented databases,” inProc. 15th Int. Conf., 1989, pp. 142–152.

  19. K.C. Kim, “Parallelism in object-oriented query processing,” inProc. 6th Int. Conf. Data Engineering, 1990, pp. 209–217.

  20. W. Kim, et al., “Integrating an object-oriented programming system with a database system,” inProc. Conf. Object-Oriented Programming Systems, Languages, and Applications, 1988, pp. 142–152.

  21. W. Kim, et al., “Architecture of the ORION next-generation database system,”IEEE Trans. Knowledge Data Eng. vol. 2, no. 1, pp. 109–124, 1990.

    Google Scholar 

  22. M. Kitsuregawa, H. Tanaka, and T. Moto-oka, “Architecture and performance of relational algebra machine GRACE,” inProc. Int. Conf. Parallel Processing, IEEE, Bellaire, MI., 1984, pp. 241–250.

    Google Scholar 

  23. M. Kitsuregawa, H. Tanaka, and T. Moto-oka, “Memory management algorithms in pipeline merge sorter,” inProc. Fourth Int. Workshop on Database Machines, D.J. DeWitt and H. Boral, (Eds.), Springer-Verlag; New York, 1985, pp. 208–232.

    Google Scholar 

  24. C. Lee, “An object flow computer for object-oriented database applications,” Ph.D. dissertation, Department of Electrical Engineering, University of Florida, 1989.

  25. D. Maier, J. Stein, A. Otis, and A. Purdy, “Development of an object-oriented DBMS,” inProc. Conf. Object-Oriented Programming Systems, Languages, and Applications, pp. 472–482, 1986.

  26. D.G. Shea, R.C. Booth, D.H. Brown, M.E. Giampapa, G.R. Irwin, T.T. Murakami, F.T. Tong, P.R. Varker, W.W. Wilcke, D.J. Zukowski, A.K. Thakore, and S.Y.W. Su, “Monitoring and simulation of processing strategies for large knowledge bases on the IBM victor multiprocessor,”Proc. Second Conf. North American Transputer Users Group, Durham, NC, 1989, pp. 11–26.

  27. S.Y.W. Su, V. Krishnamurthy, and H. Lam, “An object-oriented semantic association model (OSAM*),” inIndustrial Engineering and Manufacturing. Theoretical Issues and Applications, S. Kumar, A.L. Soyster, and R.L., Kashyap, (Eds.), American Institute of Industrial Engineering, 1989.

  28. A.K. Thakore, “Data distribution and algorithms for asynchronous parallel processing of object-oriented knowledge bases,” Ph.D. dissertation, Department of Electrical Engineering, University of Florida, 1990.

  29. A.K. Thakore and S.Y.W. Su, “Greedy heuristic mapping of object-oriented semantic schemas onto nodes of a regularly and homogeneously connected parallel architecture,” inProc. Object-Oriented Simulation Conf. La Jolla, CA. pp. 107–112, 1993.

  30. A.K. Thakore, S.Y.W. Su, and H.X. Lam, “Algorithms for asynchronous parallel processing of object-oriented databases,” submitted toIEEE Trans. Knowledge Data Eng., 1991.

  31. A.K. Thakore, S.Y.W. Su, H. Lam, and D.G. Shea, “Asynchronous parallel processing of object bases using multiple waveforms,” inProc. 1990 Int. Conf. Parallel Processing, St. Charles, ILL., vol. 1, 1990, pp. 127–135.

  32. P. Valduriez, “Join indices,”ACM Trans. Database Systems, vol. 12, no. 2, pp. 218–246, 1987.

    Google Scholar 

  33. P. Valduriez and G. Gardarin, “Join and semijoin algorithm for multiprocessor database machine,”ACM Trans. Database Systems, vol. 9, no. 1, pp. 133–161, 1987.

    Google Scholar 

  34. K. Wilkinson, et al., “The iris architecture and implementation,”IEEE Trans. Knowledge and Data Eng. vol. 2, no. 1, pp. 63–75, 1990.

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Miami, 4th Floor, McArthur Bldg., 33124-0640, Coral Gables, FL

    Arun K. Thakore

  2. Database Systems Research and Development Center, Department of Computer and Information Sciences, Department of Electrical Engineering, University of Florida, USA

    Stanley Y. W. Su

Authors
  1. Arun K. Thakore
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  2. Stanley Y. W. Su
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Thakore, A.K., Su, S.Y.W. Performance analysis of parallel object-oriented query processing algorithms. Distrib Parallel Databases 2, 59–100 (1994). https://doi.org/10.1007/BF01263339

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