Paper Info
Title
The Design and Implementation of the Ariel Active Database Rule System
Abstract
This paper describes the design and implementation of the Ariel DBMS and its tightly-coupled forward-chaining rule system. The query language of Ariel is a subset of POSTQUEL, extended with a new production-rule sublanguage. Ariel supports traditional relational database query and update operations efficiently, using a System R-like query processing strategy. In addition, the Ariel rule system is tightly coupled with query and update processing. Ariel rules can have conditions based on a mix of selections, joins, events, and transitions. For testing rule conditions, Ariel makes use of a discrimination network composed of a special data structure for testing single-relation selection conditions efficiently, and a modified version of the TREAT algorithm, called A-TREAT, for testing join conditions. The key modification to TREAT (which could also be used in the Rete algorithm) is the use of virtual驴-memory nodes which save storage since they contain only the predicate associated with the memory node instead of copies of data matching the predicate. In addition, the notions of tokens and 驴-memory nodes are generalized to support event and transition conditions. The rule-action executor in Ariel binds the data matching a rule's condition to the action of the rule at rule fire time, and executes the rule action using the query processor.
Year
DOI
Venue
1996
10.1109/69.485644
IEEE Trans. Knowl. Data Eng.
Keywords
Field
DocType
rule action,query language,active database rule system,ariel dbms,tightly-coupled forward-chaining rule system,rule condition,system r-like query processing,ariel rule,ariel rule system,rule fire time,memory node,query languages,nodes,rete algorithm,matching,electronics packaging,data structure,data bases,modification,database languages,discrimination,networks,data structures,database systems,algorithms,database management systems,relational database,relational databases,production systems,testing
Data mining,Query language,Joins,Programming language,Relational database,Computer science,Theoretical computer science,Rete algorithm,Artificial intelligence,Data structure,Executor,Active database,Machine learning,Sublanguage
Journal
Volume
Issue
ISSN
8
1
1041-4347
Citations 
PageRank 
References 
70
33.00
40
Authors
1
Name
Order
Citations
PageRank
Eric N. Hanson1917376.11