A Hadoop Extension for Analysing Spatiotemporally Referenced Events

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.SCOPUS: cp.kinfo:eu-repo/semantics/publishe

EmC-ICDSST 2019: 5th International Conference on Decision Support System Technology - ICDSST 2019 & EURO Mini Conference 2019 on "Decision Support Systems: Main Developments & Future Trends"

info:eu-repo/semantics/publishedVersio

SFNCS: A Framework for Assessment of Spatio-Temporal Visualization Methods

Movement analysis is complex due to many different factors: different forms of data, different levels of precision, strongly influenced by context, for which diverse sets of tasks require different visualizations and algorithmic approaches. There is a vast scope of previous work that researches, for diverse tasks, several approaches to visualization designs and data processing methods. The scope of tasks, potential visualization methods, and data processing that is yet to research is vast. To help reach a higher precision when describing contributions of researchers, we define a framework that characterizes information, from its recording into data to the way it is presented to the user and the terms used to communicate about it for evaluations. Within this thesis, we explain how our original research scope directed us from establishing the current state of the art for visualization methods for movement analysis while accounting for context into a characterization of visualizations, data processing methods, and communication approaches. This results in the framework that is the main contribution of our thesis. This thesis also presents several studies that refine our understanding of the impact of data complexity over diverse tasks, using precise terms. We also discuss how our system can be used to set up and analyze studies based on vague terms. Furthermore, we discuss the strength and weaknesses of existing designs for exploration tasks of contextually rich data movement, and potential design approaches to investigate in future work. These discussions include the tasks for which the designs could be most useful and how they fit within different characterizations of information and data

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium