Abstract
Most research related to parallel query processing has concentrated on how to properly partition and schedule operation-by-operation and tuple-by-tuple query processing jobs to available processors. As a result, because these operations should perform complex query optimization, tremendous overhead can be involved, especially in a massively parallel system with thousands of processors. Furthermore, there exist unnecessary dependencies among operations allocated in different processors, and a large amount of intermediate data must be exchanged among processors. This article proposes an effective deductive query processing method in a massively parallel system. For this, the facts and deductive rules of a deductive database are partitioned into fine-grain semantic elements based on the concepts of an object-oriented model. These semantic elements are used to construct an object-oriented semantic network (OOSN). Because all facts and deductive rules are mapped to the OOSN statically, a query can be evaluated effectively in a distributed manner without any complex query optimization.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Computers and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
