E.N. Hanson, "Gator: A generalized discrimination network for production database rule matching," Proc. IJCAI Workshop on Production Systems and their Innovative Applications, 1993. 35 Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
E-DEVICE: An Extensible Active Knowledge Base System with.. - Bassiliades, Vlahavas (2000) (Correct)
....token at one of its inputs, it deletes it from the corresponding memory and a negative token is output from the event. The output token contains elements only from the deleted 23 (incomplete) token because there is no need to join it with the tokens of the right memory. This deletion optimization [22] is valid because the tokens that flow among two input events contain the unique OIDs of the objects involved in the condition. Therefore, there can be no ambiguity concerning the matching of incomplete tokens. Furthermore, when an incomplete token arrives at an input node, it is matched against ....
E.N. Hanson, "Gator: A generalized discrimination network for production database rule matching," Proc. IJCAI Workshop on Production Systems and their Innovative Applications, 1993. 35
Optimizing Gator Networks for Rule Condition Testing - Eric Hanson Self-citation (Hanson) (Correct)
....way across the three nodes fi1, ff4 and ff5, then that combination would be packaged as one token and placed in the P node, triggering the rule for the new combination. The structure, pattern matching algorithms, and cost evaluation functions for Gator networks are discussed in detail elsewhere [7, 8, 17]. We have implemented Gator in Ariel and are currently testing and evaluating the new Ariel system [17] This paper presents results from a simulation conducted to test and evaluate strategies for optimizing Gator networks in an active database environment. The results of this simulation lead us ....
Eric N. Hanson. Gator: A generalized discrimination network for production rule matching. In Proceedings of the IJCAI Workshop on Production Systems and Their Innovative Applications, August 1993.
Efficient Termination Detection for Asynchronous Parallel.. - Nabeel Al-Fayoumi Self-citation (Hanson) (Correct)
....and thus, a termination algorithm has to be utilized to detect the end of trigger execution for the transaction to commit. In our case, we designed a parallel trigger system to be integrated with a parallel database system called Paradise [2] In our trigger system, we use discrimination networks [5] similar to those used in production systems [4, 7] for trigger condition matching. From a user s point of view, the result of a submitted transaction is supposed to be reported as soon as it commits with the least possible response time. In a parallel database system, the coordinator is the ....
Eric N. Hanson. Gator: A generalized discrimination network for production rule matching. In Proceedings of the IJCAI Workshop on Production Systems and Their Innovative Applications, August 1993.
Selection Predicate Indexing for Active Databases Using.. - Hanson, Johnson (1996) (5 citations) Self-citation (Hanson) (Correct)
.... to hanson cis.ufl.edu, or from http: www.cis.ufl.edu hanson IS lists lead to the development of the Rete algorithm [12] a modified version of Rete called TREAT [24] extensions to the Rete algorithm to exploit parallelism [22] and an optimized generalization of Rete and TREAT called Gator [17]. All these algorithms can be enhanced by speeding up testing of selection predicates. The problem of determining which of a set of predicates match an object arises independent of the type of data or knowledge model used. For example, facts in OPS5 are like relational tuples, whereas in KEE and ....
....the other algorithms discussed later, the boolean operators and and or are conditional. In other words, in evaluation of p and q, if p is false, then q is never evaluated. Similarly, in evaluation of p or q, if p is true, then q is never evaluated. Comments are indicated by a as in C . a. [2,17] b. 17,20] c. 8,12] d. 7,7] e. inf,17) Example intervals: 20 b a a d c c e N U L L H e a d e r inf 2 7 7 8 12 17 a, e b c e a, e e a e Figure 4: Example of an interval skip list for intervals shown. procedure findIntervals(K,L,S) x : L.header; S : OE Step down to bottom level. i : ....
Eric N. Hanson. Gator: A generalized discrimination network for production rule matching. In Proceedings of the IJCAI Workshop on Production Systems and Their Innovative Applications, August 1993.
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