Accession Number : ADA262755


Title :   Collection-Oriented Match: Scaling Up the Data in Production Systems,


Corporate Author : CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE


Personal Author(s) : Acharya, Anurag ; Tambe, Milind


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a262755.pdf


Report Date : Dec 1992


Pagination or Media Count : 39


Abstract : Match algorithms that are capable of handling large amounts of data, without giving up expressiveness are a key requirement for successful integration of relational database systems and powerful rule-based systems. Algorithms that have been used for database rule systems have usually been unable to support large and complex rule sets, while the algorithms that have been used for rule-based expert systems do not scale well with increasing amounts of data. Furthermore, these algorithms do not provide support for collection (or set) oriented production languages. This paper proposes a basic shift in the nature of match algorithms: from tuple-oriented to collection- oriented. A collection-oriented match algorithm matches each condition in a production with a collection of tuples and generates collection-oriented instantiations, i.e., instantiations that have collection of tuples corresponding to each condition in the production. This approach shows great promise for efficiently matching expressive productions against large amounts of data. In addition, it provides direct support for collection-oriented production languages.


Descriptors :   *DATA BASES , *DATA MANAGEMENT , *RULE BASED SYSTEMS , *PROTOTYPES , *DATA ACQUISITION , ALGORITHMS , COLLECTION , ARTIFICIAL INTELLIGENCE , SYSTEMS APPROACH , MATCHING , PRODUCTION


Subject Categories : Computer Programming and Software


Distribution Statement : APPROVED FOR PUBLIC RELEASE