Amirian, Pouria and Basiri, Anahid and Winstanley, Adam C. (2014) Evaluation of Data Management Systems for Geospatial Big Data. Lecture Notes in Computer Science (LNCS), 8583 . Springer Verlag, Computational Science and Its Applications – ICCSA 2014. ISBN 9783319091556

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Abstract

Big Data encompasses collection, management, processing and analysis of the huge amount of data that varies in types and changes with high frequency. Often data component of Big Data has a positional component as an important part of it in various forms, such as postal address, Internet Protocol (IP) address and geographical location. If the positional components in Big Data extensively used in storage, retrieval, analysis, processing, visualization and knowledge discovery (geospatial Big Data) the Big Data systems need certain type of techniques and algorithms for management, analytics and sharing. This paper describes the concept of geospatial Big Data management with focus on using typical and modern database management systems. Then the typical and modern types of databases for management of geospatial Big Data are evaluated based on model for storage, query languages, handling connected data, distribution models and schema evolution. As the results of the evaluations and benchmarks of this paper illustrate there is no single solution for efficient management of geospatial Big Data and in order to utilize unique characteristics of geospatial Big Data (such as topological, directional and distance relationship) a polyglot geospatial data persistence system is needed.

Item Type:	Book
Additional Information:	Cite as: Amirian P., Basiri A., Winstanley A. (2014) Evaluation of Data Management Systems for Geospatial Big Data. In: Murgante B. et al. (eds) Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8583. Springer, Cham
Keywords:	geospatial Big Data; graph database; XML document database; column-family database; spatial database; geospatial Big Data Management; polyglot geospatial data persistence;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	8083
Depositing User:	Dr. Adam Winstanley
Date Deposited:	28 Mar 2017 15:38
Publisher:	Springer Verlag
Refereed:	Yes
URI:	http://link.springer.com
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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