Forward-chaining rule systems must test each newly asserted fact against a collection of predicates to find those rules that match the fact. Expert system rule engines use a simple combination of hashing and sequential search for this matching. We introduce an algorithm for finding the matching predicates that is more efficient than the standard algorithm when the number of predicates is large. We focus on equality and inequality predicates on totally ordered domains. This algorithm is well-suited for database rule systems, where predicate-testing speed is critical. A key component of the algorithm is the interval binary search tree (IBS-tree). The IBS-tree is designed to allow efficient retrieval of all intervals (e.g. range predicates) that overlap a point, while allowing dynamic insertion and deletion of intervals. The algorithm could also be used to improve the performance of forward-chaining inference engines for large expert systems applications.

Forward-chammg rule systems must test each newly asserted fact agamst a collection of predlcates to find those rules that match the fact Expert system rule engines use a simple combmatlon of hashmg and sequential search for this matching We introduce an algorithm for findmg the matching predicates that IS more efficient than the standard algorithm when the number of predlcates 1s large We focus on equahty and mequahty predicates on totally ordered domams This algorithm 1s well-suited for database rule systems, where predicate-testing speed 1s crltlcal A key component of the algorithm 1s the rnterval bznary search tree (IBS-tree)The IBS-tree 1s deslgned to allow efficient retrieval of all intervals (e g range predicates) that overlap a point, while allowing dynamic msertlon and deletion of intervals The algorithm could a.lso be used to Improve the performance of forward-chammg inference engines for large expert systems apphcations

In this paper, we propose a design for the integration of a production rule system into an object-oriented database system. We put an emphasis on the rule system, and propose a pattern matching algorithm based on a discrimination network. A detail methodology for constructing a discrimination network for different types of rule conditions is presented.

In this paper, we propose a data structure, the Point-Range Tree PR-Dee), specifically designed f for indexing interva s. With the PR-Dee, a point data can be queried against a set of intervals to determine which of those intervals overlap the point. The PR-tree allows dynamic insertions and deletions while it maintains itself balanced. A balanced PR-Dee takes O(logn) time for search. Insertion, deletion, and storage space have worst case requirements of O(nlog n + m), O(nlog2 n + rn), and O(n logn),respectively, where n is the total number of intervals in the tree, and m the number of nodes visited during insetiion and deletion. A modified version of the PR-l&e is also developed to minimize space usage. An additional advantage of the PR-Dee is that it can be easily extended to multi-dimensional domains.

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