PARTES: A partitioning scheme for parallel matching

Author

Gallucci, Stefano ; Tan, Jack ; Hwang, Kuo-Wei

Author_Institution

Dept. of Comput. Sci. Dept., Houston Univ., TX, USA

fYear

1993

fDate

8-11 Nov 1993

Firstpage

374

Lastpage

380

Abstract

Focuses on the partitioning of rules for parallel matching in a production system. The approach, called PARTES, applies the min-cut technique to a dataflow discrimination network (DDN) that represents the antecedents of the rules. The goal is to maximize the sharing of memory nodes within a partition while minimizing the duplication of nodes across partitions. The authors illustrate the technique using a cost model based on shared memory nodes defined in a DDN created by the Rete algorithm. A generalization to any other algorithm utilizing a DDN is straightforward. A performance analysis is provided to show the effectiveness of PARTES

Keywords

generalisation (artificial intelligence); knowledge representation; minimax techniques; parallel algorithms; pattern matching; shared memory systems; PARTES; Rete algorithm; cost model; dataflow discrimination network; generalization; memory node sharing; min-cut technique; node duplication; parallel matching; performance analysis; production system; rule antecedent representation; rule partitioning scheme; Clustering algorithms; Costs; Databases; Monitoring; Partitioning algorithms; Performance analysis; Performance evaluation; Production systems; Real time systems; Software algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 1993. TAI '93. Proceedings., Fifth International Conference on

Conference_Location

Boston, MA

ISSN

1063-6730

Print_ISBN

0-8186-4200-9

Type

conf

DOI

10.1109/TAI.1993.633983

Filename

633983