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Analysing real world data streams with spatio-temporal correlations: Entropy vs. Pearson correlation

Bermudez-Edo, Maria, Barnaghi, Payam and Moessner, Klaus (2018) Analysing real world data streams with spatio-temporal correlations: Entropy vs. Pearson correlation Automation in Construction, 88. pp. 87-100.

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Abstract

Smart Cities use different Internet of Things (IoT) data sources and rely on big data analytics to obtain information or extract actionable knowledge crucial for urban planners for efficiently use and plan the construction infrastructures. Big data analytics algorithms often consider the correlation of different patterns and various data types. However, the use of different techniques to measure the correlation with smart cities data and the exploitation of correlations to infer new knowledge are still open questions. This paper proposes a methodology to analyse data streams, based on spatio-temporal correlations using different correlation algorithms and provides a discussion on co-occurrence vs. causation. The proposed method is evaluated using traffic data collected from the road sensors in the city of Aarhus in Denmark.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Bermudez-Edo, Maria
Barnaghi, PayamP.Barnaghi@surrey.ac.uk
Moessner, KlausK.Moessner@surrey.ac.uk
Date : 11 January 2018
Identification Number : 10.1016/j.autcon.2017.12.036
Copyright Disclaimer : © 2018 Elsevier B.V. All rights reserved.
Uncontrolled Keywords : Smart cities; Internet of things; Correlation; Entropy
Depositing User : Clive Harris
Date Deposited : 16 Jan 2018 13:48
Last Modified : 14 Mar 2018 15:46
URI: http://epubs.surrey.ac.uk/id/eprint/845622

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