Abstract
Rule-based systems have been used extensively by the AI community in implementing knowledge-based expert systems. A current trend in the database community is to use rules for the purpose of providing inferential capabilities for large database applications. However, in the database context, performance of rule-program processing has proved to be a major stumbling block particularly in data-intensive and real-time applications. Similar work in the AI community has demonstrated the same problems mainly due to the predominantly sequential semantics of the underlying rule languages. In a previous paper, we presented an incremental evaluation algorithm forDatalog ⌝* programs. We show the general applicability of the incremental evaluation algorithm to a broad class ofDatalog-style rule languages with varying operational semantics, denoted byDatalog** to emphasize this generality. We present the proof of correctness of the algorithm within the Datalog** frame work, and its extension for the PARULEL rule language, a principle component of the PARADISER (PARAllel and DIStributed Environment for Rules) rule processing environment for databases. We discuss the meta-rule formalism of PARULEL and argue that it provides a means for programmable operational semantics which separates control from the base logic of a rule program. This allows the realization of a wide range of operational semantics including those of Datalog and OPS5.
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References
Abiteboul, S. and Simon E., (1991). Fundamental Properties of Deterministic and Nondeterministic Extensions of Datalog*.Journal of Theoretical Computer Science.
Apt, K.R. and Pugin, J. (1987). Maintenance of stratified databases viewed as a belief revision system.Proc. 6th ACM Symp. PODS. (pp. 136–145).
Barr, et al., InThe Handbook of Artificial Intelligence, Vol. 2. (pp. 72-76). Reading, MA: AddisonWesley.
Chandy, K.M. and Misra, J.On Proofs of Distributed Algorithms with Application to the Problem of Termination Detection. Technical Report. Dept. of Computer Science, University of Texas, Austin.
Cohen, S. and Wolfson, O. (1989). Why a single parallelization strategy is not enough in knowledgebases.Proc. 8th ACM Symp. PODS. (pp. 200–217).
Inference Corporation (1990).ART-IM Programming Language Reference Manual. Technical Report, Inference Corporation.
Davis, R. (1980). Meta-Rules: Reasoning about Control.Artificial Intelligence, 15, 179–222.
Delcambre, L. (1988). RPL: An Expert System Language with Query Power.IEEE Expert.
Dewan, H.M., Ohsie, D.A. Stolfo S.J. and DaSilva, S. (1992). TheDesign and Implementation of Paradiser vO.O. Technical Report. Department of Computer Science, Columbia University.
Doyle, J. (1981). A Truth Maintenance System. InReadings in Artificial Intelligence. Morgan Kaufmann.
Francez, N. (1980). Distributed Termination. InTransactions on Programming Languages and Systems. (pp. 42–55). ACM.
Haas, L., Freytag, G., Lohman., H., and Pirahesh, H. (1989). Extensible query processing in starburst.Proc. ACM SIGMOD 1989, Int. Conf. Management of Data. ACM Press.
Ishida, T. and Stolfo, S.J. (1985). Towards the parallel execution of rules in production system programs.Proc. Int. Conf. Parallel Processing (pp. 568–575). IEEE.
Kiernan, G., de Maindreville, C., and Simon, E. (1990). Making deductive database a practical technology: a step forward.Proc. ACM-SIGMOD 1990. Int. Conf. Management of Data (pp. 237–246).
Maier, D. and Warren, D.S.,Computing with Logic: Introduction to Logic Programming. Redwood City, CA: Benjamin-Cummings.
Miranker, D.P. and Lofaso, B.J., (to appear). The Organization and Performance of a TREAT Based Production System Compiler.IEEE Transactions on Knowledge and Data Engineering.
Miranker, D.P., Lofaso, B., Farmer, G., Chandra, A. and Brant, D. (1990). On a TREAT based production system compiler. Proc.10th Int. Conf. Expert Systems (pp. 617–630).
Miranker, D.P. (1986).TREAT: A New and Efficient Match Algorithm for AI Production Systems. Ph.D. thesis, Department of Computer Science, Columbia University.
Reiter R. and de Kleer, J. (1987). Foundations of assumption based truth maintenance systems: Preliminary report.Proc. Nat. Conf. Artif. Intell. AAAI.
Sellis, C., Lin, C., and Raschid, L. (1989). Implementing large production systems in a dbms environment: Concepts and Algorithms.Proc. ACM SIGMOD 1989, Int. Conf. Management of Data. ACM Press.
Stolfo, S., Miranker, D.P., and Mills, R. (1985). A simple processing scheme to extract and load balance implicit parallelism in the concurrent match of production rules.Proc. AFIPS Symp. Fifth Generation Computing.
Stolfo, S., Wolfson, O., Chan, P., Dewan, H., Woodbury, L., Glazier, J., and Ohsie, D. (1991a). Parulel: Parallel Rule Processing Using Meta-Rules for Redaction.Journal of Parallel and Distributed Computing, 13, 366–382.
Stolfo, S.J., Dewan, H.M., and Wolfson, O. (1991b). The PARULEL parallel rule language.Proc. IEEE Int. Conf. Parallel Processing (pp. II:36–45). IEEE.
Stolfo, S. (1984). Learning Control of Production Systems.Cognition and Brain Theory.
Stonebraker, M. and Kemnitz, G. 1991. The POSTGRES Next-Generation DBMS.Communications of the ACM.
Ullman, J.D. 1989.Principles of Database and Knowledge-Base Systems Vol. 2. Computer Science Press. Wolfson, O. and Ozeri, A. 1990. A new paradigm for parallel and distributed rule-processing.Proc,ACM-SIGMOD 1990. Int. Conf. Management of Data (pp. 133–142).
Wolfson, O., Dewan, H., Stolfo, S., and Yemini, Y. (1991). Incremental evaluation of rules and its relationship to parallelism.Proc. ACM-SIGMOD 1991, Int. Conf. Management of Data (pp. 78–87).
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Dewan, H.M., Ohsie, D., Stolfo, S.J. et al. Incremental database rule processing in PARADISER. J Intell Inf Syst 1, 177–209 (1992). https://doi.org/10.1007/BF00962282
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DOI: https://doi.org/10.1007/BF00962282