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PairFac: Event analytics through discriminant tensor factorization

Wen, X and Lin, YR and Pelechrinis, K (2016) PairFac: Event analytics through discriminant tensor factorization. In: UNSPECIFIED.

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The study of disaster events and their impact in the urban space has been traditionally conducted through manual collections and analysis of surveys, questionnaires and authority documents. While there have been increasingly rich troves of human behavioral data related to the events of interest, the ability to obtain hindsight following a disaster event has not been scaled up. In this paper, we propose a novel approach for analyzing events called PairFac. PairFac utilizes discriminant tensor analysis to automatically discover the impact of a major event from rich human behavioral data. Our method aims to (i) uncover the persistent patterns across multiple interrelated aspects of urban behavior (e.g., when, where and what citizens do in a city) and at the same time (ii) identify the salient changes following a potentially impactful event. We show the effectiveness of PairFac in comparison with previous methods through extensive experiments. We also demonstrate the advantages of our approach through case studies with real-world traffic sensor data and social media streams surrounding the 2015 terrorist attacks in Paris. Our work has both methodological contributions in studying the impact of an external stimulus on a system as well as practical implications in the area of disaster event analysis and assessment.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Wen, X
Lin, YRYURULIN@pitt.eduYURULIN0000-0002-8497-3015
Pelechrinis, Kkpele@pitt.eduKPELE0000-0002-6443-3935
ContributionContributors NameEmailPitt UsernameORCID
CorrespondentLin, Yu-Ruyurulin@pitt.eduYURULINUNSPECIFIED
Date: 24 October 2016
Date Type: Publication
Journal or Publication Title: International Conference on Information and Knowledge Management, Proceedings
Volume: 24-28-
Page Range: 519 - 528
Event Type: Conference
DOI or Unique Handle: 10.1145/2983323.2983837
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9781450340731
Date Deposited: 30 Jun 2017 14:58
Last Modified: 18 Sep 2021 04:57


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