A Hadoop Extension for Analysing Spatiotemporally Referenced Events

Abstract

A spatiotemporally referenced event is a tuple that contains both a spatial reference and a temporal reference. The spatial reference is typically a point coordinate, and the temporal reference is a timestamp. The event payload can be the reading of a sensor (IoT systems), a user comment (geo-tagged social networks), a news article (gdelt), etc. Spatiotemporal event datasets are ever growing, and the requirements for their processing goes beyond traditional client-sever GIS architectures. Rather, Hadoop-like architectures shall be used. Yet, Hadoop does not provide the types and operations necessary for processing such datasets. In this paper, we propose a Hadoop extension (indeed a SpatialHadoop extension) capable of performing analytics on big spatiotemporally referenced event dataset. The extension includes data types and operators that are integrated into the Hadoop core, to be used as natives. We further optimize the querying by means of a spatiotemporal index. Experiments on the gdelt event dataset demonstrate the utility of the proposed extension.