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SPQUSAR : A Large-Scale Qualitative Spatial Reasoner Using Apache Spark
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 Title & Authors
SPQUSAR : A Large-Scale Qualitative Spatial Reasoner Using Apache Spark
Kim, Jongwhan; Kim, Jonghoon; Kim, Incheol;
 
 Abstract
In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner using Apache Spark, an in-memory high speed cluster computing environment, which is effective for sequencing and iterating component reasoning jobs. The proposed reasoner can not only check the integrity of a large-scale spatial knowledge base representing topological and directional relationships between spatial objects, but also expand the given knowledge base by deriving new facts in highly efficient ways. In general, qualitative reasoning on topological and directional relationships between spatial objects includes a number of composition operations on every possible pair of disjunctive relations. The proposed reasoner enhances computational efficiency by determining the minimal set of disjunctive relations for spatial reasoning and then reducing the size of the composition table to include only that set. Additionally, in order to improve performance, the proposed reasoner is designed to minimize disk I/Os during distributed reasoning jobs, which are performed on a Hadoop cluster system. In experiments with both artificial and real spatial knowledge bases, the proposed Spark-based spatial reasoner showed higher performance than the existing MapReduce-based one.
 Keywords
qualitative spatial reasoning;spatial knowledge base;apache spark;
 Language
Korean
 Cited by
1.
Unified Platform for AI and Big Data Analytics, Journal of Computer and Communications, 2017, 05, 08, 1  crossref(new windwow)
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