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CorClustST - Correlation-based clustering of big spatio-temporal datasets

Hüsch, Marc and Schyska, Bruno and von Bremen, Lüder (2018) CorClustST - Correlation-based clustering of big spatio-temporal datasets. Future Generation Computer Systems-the International Journal of Grid Computing and Escience, pp. 1-10. Elsevier. DOI: 10.1016/j.future.2018.04.002 ISSN 0167-739X

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Official URL: https://www.sciencedirect.com/science/article/pii/S0167739X17313353?via%3Dihub

Abstract

Increasing amounts of high-velocity spatio-temporal data reinforce the need for clustering algorithms which are effective for big data processing and data reduction. As currently applied spatio-temporal clustering algorithms have certain drawbacks regarding the comparability of the results, we propose an alternative spatio-temporal clustering technique which is based on empirical spatial correlations over time. As a key feature, CorClustST makes it easily possible to compare and interpret clustering results for different scenarios such as multiple underlying variables or varying time frames. In a test case, we show that the clustering strategy successfully identifies increasing spatial correlations of wind power forecast errors in Europe for longer forecast horizons. An extension of the clustering algorithm is finally presented which allows for a large-scale parallel implementation and helps to circumvent memory limitations. The proposed method will especially be helpful for researchers who aim to preprocess big spatio-temporal datasets and who intend to compare clustering results and spatial dependencies for different scenarios.

Item URL in elib:https://elib.dlr.de/130950/
Document Type:Article
Title:CorClustST - Correlation-based clustering of big spatio-temporal datasets
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Hüsch, Marcmarc.huesch (at) tu-dortmund.deUNSPECIFIED
Schyska, Brunobruno.schyska (at) dlr.deUNSPECIFIED
von Bremen, Lüderlueder.von.bremen (at) dlr.deUNSPECIFIED
Date:7 April 2018
Journal or Publication Title:Future Generation Computer Systems-the International Journal of Grid Computing and Escience
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1016/j.future.2018.04.002
Page Range:pp. 1-10
Editors:
EditorsEmail
Zissis, Dimitriosdzissis@aegean.gr
Publisher:Elsevier
ISSN:0167-739X
Status:Published
Keywords:Clustering Big spatio-temporal data Spatial dependence Preprocessing Data reduction
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - Energie und Verkehr
Location: Oldenburg
Institutes and Institutions:Institute of Networked Energy Systems > Energy Systems Analysis
Deposited By: von Bremen, Lüder
Deposited On:16 Dec 2019 12:30
Last Modified:16 Dec 2019 12:30

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