Premium
GISQAF: MapReduce guided spatial query processing and analytics system
Author(s) -
AlNaami Khaled Mohammed,
Seker Sadi Evren,
Khan Latifur
Publication year - 2016
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2383
Subject(s) - computer science , geospatial analysis , analytics , scalability , spatial query , big data , cloud computing , query optimization , database , event (particle physics) , query language , relational database management system , query expansion , spatial database , spatial analysis , sargable , data mining , web search query , information retrieval , relational database , search engine , operating system , physics , cartography , quantum mechanics , geography , remote sensing , geology
Summary The Global Database of Event, Language, and Tone (GDELT) is the only global political georeferenced event dataset with more than 250 million observations covering all countries in the world since January 1, 1979. TABARI and CAMEO are the tools that are used to collect and code events from all international news coverage. To query such big geospatial data, traditional RDBMS can no longer be used, and the need for parallel distributed solutions has become a necessity. MapReduce paradigm has proven to be a scalable platform to process and analyze Big Data in the cloud. Hadoop, as an implementation of MapReduce, is an open‐source application that has been widely used and accepted in academia and industry. However, when dealing with Spatial Data, Hadoop is not equipped well and does not perform efficiently. SpatialHadoop is an extension of Hadoop with the support of spatial data. In this paper, we present Geographic Information System Query and Analytics Framework (GISQAF), which has been built on top of SpatialHadoop. GISQAF focuses on two parts: query processing and data analytics. For the query processing part, we show how this solution outperforms Hadoop query processing by orders of magnitude when applying queries on the GDELT dataset with a size of 60 GB. We show the results for various types of queries. For the data analytics part, we present an approach for finding Spatial co‐occurring events. We show how GISQAF is suitable and efficient to handle data analytics techniques. Copyright © 2015 John Wiley & Sons, Ltd.