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Open AccessArticle

Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data

Department of Computer Science, Stellenbosch University, Stellenbosch 7600, South Africa
Institute for Information Systems, TU Braunschweig, D-38106 Braunschweig, Germany
Author to whom correspondence should be addressed.
Future Internet 2020, 12(5), 78;
Received: 30 March 2020 / Revised: 23 April 2020 / Accepted: 23 April 2020 / Published: 26 April 2020
(This article belongs to the Special Issue Sense Making in the Digital World)
Every day large quantities of spatio-temporal data are captured, whether by Web-based companies for social data mining or by other industries for a variety of applications ranging from disaster relief to marine data analysis. Making sense of all this data dramatically increases the need for intelligent backend systems to provide realtime query response times while scaling well (in terms of storage and performance) with increasing quantities of structured or semi-structured, multi-dimensional data. Currently, relational database solutions with spatial extensions such as PostGIS, seem to come to their limits. However, the use of graph database technology has been rising in popularity and has been found to handle graph-like spatio-temporal data much more effectively. Motivated by the need to effectively store multi-dimensional, interconnected data, this paper investigates whether or not graph database technology is better suited when compared to the extended relational approach. Three database technologies will be investigated using real world datasets namely: PostgreSQL, JanusGraph, and TigerGraph. The datasets used are the Yelp challenge dataset and an ambulance response simulation dataset, thus combining real world spatial data with realistic simulations offering more control over the dataset. Our extensive evaluation is based on how each database performs under practical data analysis scenarios similar to those found on enterprise level. View Full-Text
Keywords: graph databases; spatio-temporal data; NoSQL; yelp dataset graph databases; spatio-temporal data; NoSQL; yelp dataset
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Baker Effendi, S.; van der Merwe, B.; Balke, W.-T. Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data. Future Internet 2020, 12, 78.

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