• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.237-242
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• 2019

Spark variance technique is melded the jagged spill-over-sonance status of the glitter-differentiation knowledge level (GDKL) on the spark knowledge gestalt. The knowledge level condition by the spark knowledge gestalt system is comprised with the spill-over-sonance system. As to search a spot of the glitter situation, we are obtained of the spark value with black-red dot by the spill-over upper structure. The concept of knowledge level is comprised the reference of glitter-differentiation level for variance signal by the spark sonance gestalt. Further presenting a jagged variance of the GDKL of the maximum in terms of the spill-over-sonance gestalt, and spark spot sonance that was the a spark value of the far variance of the Spa-kg-FA-${\rho}_{MAXN}$ with $17.68{\pm}2.22units$, that was the a spark value of the convenient variance of the Spa-kg-CO-${\rho}_{MAXN}$ with $7.55{\pm}0.59units$, that was the a spark value of the flank variance of the Spa-kg-FL-${\rho}_{MAX}$ with $2.70{\pm}0.48units$, that was the a spark value of the vicinage variance of the Spa-kg-VI-${\rho}_{MAX}$ with $0.48{\pm}0.05units$. The spill-over sonance will be to appraisal at the jagged ability of the spill-over-sonance gestalt with black-red dot by the spark knowledge level on the GDKL that is presented the glitter-differentiation gestalt by the knowledge level system. Spill-over knowledge system will be possible to restrain of a gestalt by the special signal and to employ a spark data of spill-over sonance level.

• Journal of KIISE
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• v.42 no.7
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• pp.852-859
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• 2015

As the amount of knowledge information significantly increases, a lot of progress has been made in the studies focusing on how to reason large scale ontology effectively at the level of RDFS or OWL. These reasoning methods are divided into TBox classifications and ABox realizations. A TBox classification mainly deals with integrity and dependencies in schema, whereas an ABox realization mainly handles a variety of issues in instances. Therefore, the ABox realization is very important in practical applications. In this paper, we propose a realization method for analyzing the constraint of the specified class, so that the reasoning system automatically infers the classes to which instances belong. Unlike conventional methods that take advantage of the object oriented language based distributed file system, we propose a large scale ontology reasoning method using spark, which is a functional programming-based in-memory system. To verify the effectiveness of the proposed method, we used instances created from the Wine ontology by W3C(120 to 600 million triples). The proposed system processed the largest 600 million triples and generated 951 million triples in 51 minutes (696 K triple / sec) in our largest experiment.