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Optimization of Materialization Strategies for Derived Data Elements
April 1996 (vol. 8 no. 2)
pp. 260-272

Abstract—The research in materialization of derived data elements has dealt so far with the if issue; that is, the question whether to physically store derived data elements. In the active database area, there has been some research on the how issue. In this paper, we deal with the when issue, devising an optimization model to determine the optimal materialization strategy. The decision problem confronted by the optimization model is more complex than "to materialize or not to materialize." The decision problem deals with devising the materialization strategy that consists of a set of interdependent decisions about each derived data element. Each decision relates to two issues:

  • Should the value of a derived data element be persistent?

  • What is the required level of consistency of a derived value with respect to its derivers?

  • For each derived data element, the decision is based on both its local properties (complexity of derivation, update and retrieval frequencies, etc.) and its interdependencies with other derived values. The optimization model is based on a heuristic algorithm that finds a local optimum (which is a global optimum in many cases) in O(N2) and a monitor that obtains feedback about the actual database performance. This optimization model is general and is not specific to any data model. Our experimental results show that a predictor for the optimal solution cannot be obtained in any intuitive or analytic way, due to the complexity of the involved considerations; thus, there is no obvious way to achieve these results without using the optimization model. This fact is a strong motivation for applying such an optimization model. Our experimental results further indicate that the optimization model is useful in the sense that the system performance (with respect to the applications' goal function) is substantially improved compared to any universal materialization policy.

    [1] R. Alonso, D. Barbara, and H. Garcia-Molina, "Data Caching Issues in an Information Retrieval System," ACM Trans. Database Systems, vol. 15, no. 3, pp. 359-384, Sept. 1990.
    [2] J.A. Blakeley, P. Larson, and F.W. Tompa, "Efficiently Updating Materialized Views," Proc. ACM SIGMOD Conf.,Washington, D.C., pp. 61-71, May 1986.
    [3] J.A. Blakeley, N. Coburn, and P.-A. Larson, “Updating Derived Relations: Detecting Irrelevant and Autonomously Computable Updates,” ACM Trans. Database Systems, vol. 14, no. 3, pp. 369-400, Sept. 1989.
    [4] D. Botzer, "Optimization of Knowledge and Data Representation in Active Databases," MSc thesis, Technion—Israel Inst. of Tech nology, July 1992.
    [5] J.J. Carey, R. Jauhari, and M. Livny, "On Transaction Boundaries in Active Database: A Performance Perspective," Univ. of Wisconsin Technical Report No. CS-791, 1988.
    [6] "CODASYL," Data Base Task Group Report, ACM, Apr. 1971.
    [7] O. Etzion, "Flexible Consistency Modes for Active Database Applications," Information Systems, vol. 18, no. 6, pp. 391-404, Nov. 1993.
    [8] O. Etzion, "PARDES—A Data-Driven Oriented Active Database Model," SIGMOD Record, Mar. 1993.
    [9] E.N. Hanson, "A Performance Analysis of View Materialization Strategies," Proc. ACM SIGMOD Conf.,San Francisco, pp. 440-453, 1987.
    [10] E.N. Hanson,"Rule condition testing and action execution in Ariel," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 49-58, June 1992.
    [11] E.N. Hanson, "Gator: A Generalized Discrimination Network for Production Rule Matching," Proc. IJCAI Workshop Production Systems and Their Innovative Applications,Chambry, France, Aug. 1993.
    [12] S. Hudson and R. King, "CACTIS: A Database System for Specifying Functionally Defined Data," Proc. IEEE OODBS Workshop, 1986.
    [13] G. Lohman et al., "Extensions to Starburst: Objects, Types, Functions, and Rules," Comm. ACM, vol. 34, no. 10, 1991.
    [14] J. Srivastava and D. Rotem, "Analytical Modelling of Materialized View Maintenance Algorithms," Proc. PODS, 1988.
    [15] A. Segev and J. Park, "Updating Distributed Materialized Views," IEEE Trans. Knowledge and Data Eng., vol. 1, no. 2, pp. 173-184, June 1989.
    [16] A. Segev and J.L. Zhao, "Data Management for Large Rule Systems," Proc. VLDB, 1991.
    [17] A. Segev and W. Fang, "Optimal Update Policies for Distributed Materialized Views," Management Science, vol. 37, no. 7, July 1991.
    [18] T. Sellis,C.-C. Lin, and L. Raschid,"Data intensive production systems: The DIPS approach," SIGMOD Record, pp. 52-58, Sept. 1989.
    [19] O. Shmueli and A. Itai, "Maintenance of Views," Proc. ACM SIGMOD, 1984.
    [20] M. Stonebraker and G. Kemnitz,"The POSTGRES next-generation database management system," Comm. ACM, vol. 34, no. 10, pp. 78-92, Oct. 1991.
    [21] Xerox Advanced Information Tech nologies, "HiPAC: A Research Project in Active, Time-Constrained Database Management," Final Technical Report No. XAIT-89-02, July 1989.

    Index Terms:
    Database tuning, materialization strategies, derived data, active databases, database optimization.
    David Botzer, Opher Etzion, "Optimization of Materialization Strategies for Derived Data Elements," IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 2, pp. 260-272, April 1996, doi:10.1109/69.494165
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