MRSPAKE : A Web-Scale Spatial Knowledge Extractor Using Hadoop MapReduce


KIPS Transactions on Software and Data Engineering, Vol. 5, No. 11, pp. 569-584, Nov. 2016
10.3745/KTSDE.2016.5.11.569,   PDF Download:
Keywords: Spatial Knowledge Extraction, Topological Relation, Directional Relation
Abstract

In this paper, we present a spatial knowledge extractor implemented in Hadoop MapReduce parallel, distributed computing environment. From a large spatial dataset, this knowledge extractor automatically derives a qualitative spatial knowledge base, which consists of both topological and directional relations on pairs of two spatial objects. By using R-tree index and range queries over a distributed spatial data file on HDFS, the MapReduce-enabled spatial knowledge extractor, MRSPAKE, can produce a web-scale spatial knowledge base in highly efficient way. In experiments with the well-known open spatial dataset, Open Street Map (OSM), the proposed web-scale spatial knowledge extractor, MRSPAKE, showed high performance and scalability


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Cite this article
[IEEE Style]
S. Lee and I. Kim, "MRSPAKE : A Web-Scale Spatial Knowledge Extractor Using Hadoop MapReduce," KIPS Transactions on Software and Data Engineering, vol. 5, no. 11, pp. 569-584, 2016. DOI: 10.3745/KTSDE.2016.5.11.569.

[ACM Style]
Seok-Jun Lee and In-Cheol Kim. 2016. MRSPAKE : A Web-Scale Spatial Knowledge Extractor Using Hadoop MapReduce. KIPS Transactions on Software and Data Engineering, 5, 11, (2016), 569-584. DOI: 10.3745/KTSDE.2016.5.11.569.