Matches in SemOpenAlex for { <https://semopenalex.org/work/W2340764580> ?p ?o ?g. }
Showing items 1 to 67 of
67
with 100 items per page.
- W2340764580 endingPage "129" @default.
- W2340764580 startingPage "129" @default.
- W2340764580 abstract "Expert systems with large knowledge bases have necessitated the use of database technology for efficient knowledge management. Emerging applications like process control, battle management, and office automation require rule-based reasoning on a large disk-based database. Forward chaining production systems, e.g. OPS5, are commonly used to implement expert systems. The close match between the data model of the OPS5 production language and the relational model makes their integration highly desirable. However, existing techniques in both domains are unsuitable for a successful integration.A production system consists of rules, defined as $langle condition, actionrangle$ pairs, that repeatedly performs a three-phase match-select-execute cycle. Each rule is matched against the data stored in a global database (working memory) to determine whether its condition is satisfied. If so, the rule is inserted into the conflict set. One rule is selected in the select phase for firing in the execution phase. Matching is a time consuming activity even in a main memory environment whose degraded performance is further aggravated in secondary memory. This thesis focuses on both the static and dynamic aspects of matching. The static aspect concerns the construction of an efficient matching network while the dynamic nature addresses efficient matching.It is known that the static aspect is NP-Hard and is also important in other domains. We will show that there exists an equivalence in matching problems in other domains, e.g., the view update problem, multi-query optimization and integrity maintenance in database systems, and that a solution in one domain is also applicable to the other domains.The focus on the dynamic aspect is on the design of efficient algorithms for matching due to the bottleneck in a disk-based environment. Typical matching algorithms are either tuple space based or binding space based. Tuple space based matchings are space efficient at the expense of slow convergence while binding space based ones have fast convergence but are space inefficient. We propose a hybrid space matching algorithm that yields fast convergence and is space efficient. Our matching algorithm is also applicable in the other domains as well. We also provide simulation results to support our observations." @default.
- W2340764580 created "2016-06-24" @default.
- W2340764580 creator A5002187701 @default.
- W2340764580 creator A5021296126 @default.
- W2340764580 date "1990-01-01" @default.
- W2340764580 modified "2023-09-26" @default.
- W2340764580 title "On efficient matching and optimization of rule sets in ai and databases" @default.
- W2340764580 hasPublicationYear "1990" @default.
- W2340764580 type Work @default.
- W2340764580 sameAs 2340764580 @default.
- W2340764580 citedByCount "0" @default.
- W2340764580 crossrefType "journal-article" @default.
- W2340764580 hasAuthorship W2340764580A5002187701 @default.
- W2340764580 hasAuthorship W2340764580A5021296126 @default.
- W2340764580 hasConcept C105795698 @default.
- W2340764580 hasConcept C124101348 @default.
- W2340764580 hasConcept C129916263 @default.
- W2340764580 hasConcept C154945302 @default.
- W2340764580 hasConcept C165064840 @default.
- W2340764580 hasConcept C33923547 @default.
- W2340764580 hasConcept C41008148 @default.
- W2340764580 hasConcept C4554734 @default.
- W2340764580 hasConcept C46743427 @default.
- W2340764580 hasConcept C5655090 @default.
- W2340764580 hasConcept C58328972 @default.
- W2340764580 hasConcept C77088390 @default.
- W2340764580 hasConceptScore W2340764580C105795698 @default.
- W2340764580 hasConceptScore W2340764580C124101348 @default.
- W2340764580 hasConceptScore W2340764580C129916263 @default.
- W2340764580 hasConceptScore W2340764580C154945302 @default.
- W2340764580 hasConceptScore W2340764580C165064840 @default.
- W2340764580 hasConceptScore W2340764580C33923547 @default.
- W2340764580 hasConceptScore W2340764580C41008148 @default.
- W2340764580 hasConceptScore W2340764580C4554734 @default.
- W2340764580 hasConceptScore W2340764580C46743427 @default.
- W2340764580 hasConceptScore W2340764580C5655090 @default.
- W2340764580 hasConceptScore W2340764580C58328972 @default.
- W2340764580 hasConceptScore W2340764580C77088390 @default.
- W2340764580 hasLocation W23407645801 @default.
- W2340764580 hasOpenAccess W2340764580 @default.
- W2340764580 hasPrimaryLocation W23407645801 @default.
- W2340764580 hasRelatedWork W1571270363 @default.
- W2340764580 hasRelatedWork W1596125699 @default.
- W2340764580 hasRelatedWork W1600251413 @default.
- W2340764580 hasRelatedWork W1600780797 @default.
- W2340764580 hasRelatedWork W175405321 @default.
- W2340764580 hasRelatedWork W2111035195 @default.
- W2340764580 hasRelatedWork W2145335345 @default.
- W2340764580 hasRelatedWork W2145594740 @default.
- W2340764580 hasRelatedWork W2281228347 @default.
- W2340764580 hasRelatedWork W2315830939 @default.
- W2340764580 hasRelatedWork W2339678056 @default.
- W2340764580 hasRelatedWork W2401165632 @default.
- W2340764580 hasRelatedWork W2470401011 @default.
- W2340764580 hasRelatedWork W2534870421 @default.
- W2340764580 hasRelatedWork W2566242334 @default.
- W2340764580 hasRelatedWork W2965868507 @default.
- W2340764580 hasRelatedWork W2972859423 @default.
- W2340764580 hasRelatedWork W3088017710 @default.
- W2340764580 hasRelatedWork W3152674419 @default.
- W2340764580 hasRelatedWork W375045620 @default.
- W2340764580 isParatext "false" @default.
- W2340764580 isRetracted "false" @default.
- W2340764580 magId "2340764580" @default.
- W2340764580 workType "article" @default.