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Conference Paper: Anomaly detection in GPS data based on visual analytics
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TitleAnomaly detection in GPS data based on visual analytics
 
AuthorsLiao, Z1
Yu, Y1
Chen, B2
 
KeywordsH.1.2 [models and principles]: user/Machine systems - human information processing
H.5.2 [information interfaces and presentation]: user interfaces - graphics user interfaces
I.5.2 [pattern recognition]: design methodology - pattern analysis, feature evaluation and selection
 
Issue Date2010
 
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001630
 
CitationThe 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST), Salt Lake City, UT., 25-26 October 2010. In Proceedings of the IEEE VAST Symposium, 2010, p. 51-58 [How to Cite?]
DOI: http://dx.doi.org/10.1109/VAST.2010.5652467
 
AbstractModern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction, while human experts hold the advantage of possessing high-level intelligence and domain-specific expertise. We combine the power of the two for anomaly detection in GPS data by integrating them through a visualization and human-computer interaction interface. In this paper we introduce GPSvas (GPS Visual Analytics System), a system that detects anomalies in GPS data using the approach of visual analytics: a conditional random field (CRF) model is used as the machine learning component for anomaly detection in streaming GPS traces. A visualization component and an interactive user interface are built to visualize the data stream, display significant analysis results (i.e., anomalies or uncertain predications) and hidden information extracted by the anomaly detection model, which enable human experts to observe the real-time data behavior and gain insights into the data flow. Human experts further provide guidance to the machine learning model through the interaction tools; the learning model is then incrementally improved through an active learning procedure. ©2010 IEEE.
 
ISBN978-1-4244-9487-3
 
DOIhttp://dx.doi.org/10.1109/VAST.2010.5652467
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorLiao, Z
 
dc.contributor.authorYu, Y
 
dc.contributor.authorChen, B
 
dc.date.accessioned2011-09-23T06:04:35Z
 
dc.date.available2011-09-23T06:04:35Z
 
dc.date.issued2010
 
dc.description.abstractModern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction, while human experts hold the advantage of possessing high-level intelligence and domain-specific expertise. We combine the power of the two for anomaly detection in GPS data by integrating them through a visualization and human-computer interaction interface. In this paper we introduce GPSvas (GPS Visual Analytics System), a system that detects anomalies in GPS data using the approach of visual analytics: a conditional random field (CRF) model is used as the machine learning component for anomaly detection in streaming GPS traces. A visualization component and an interactive user interface are built to visualize the data stream, display significant analysis results (i.e., anomalies or uncertain predications) and hidden information extracted by the anomaly detection model, which enable human experts to observe the real-time data behavior and gain insights into the data flow. Human experts further provide guidance to the machine learning model through the interaction tools; the learning model is then incrementally improved through an active learning procedure. ©2010 IEEE.
 
dc.description.naturepublished_or_final_version
 
dc.description.otherThe 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST), Salt Lake City, UT., 25-26 October 2010. In Proceedings of the IEEE VAST Symposium, 2010, p. 51-58
 
dc.identifier.citationThe 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST), Salt Lake City, UT., 25-26 October 2010. In Proceedings of the IEEE VAST Symposium, 2010, p. 51-58 [How to Cite?]
DOI: http://dx.doi.org/10.1109/VAST.2010.5652467
 
dc.identifier.doihttp://dx.doi.org/10.1109/VAST.2010.5652467
 
dc.identifier.epage58
 
dc.identifier.hkuros194328
 
dc.identifier.isbn978-1-4244-9487-3
 
dc.identifier.scopuseid_2-s2.0-78650933347
 
dc.identifier.spage51
 
dc.identifier.urihttp://hdl.handle.net/10722/140003
 
dc.languageeng
 
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001630
 
dc.relation.ispartofProceedings of the IEEE Symposium on Visual Analytics Science and Technology, VAST 2010
 
dc.relation.referencesReferences in Scopus
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.rightsProceedings of the IEEE Symposium on Visual Analytics Science and Technology. Copyright © IEEE.
 
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
 
dc.subjectH.1.2 [models and principles]: user/Machine systems - human information processing
 
dc.subjectH.5.2 [information interfaces and presentation]: user interfaces - graphics user interfaces
 
dc.subjectI.5.2 [pattern recognition]: design methodology - pattern analysis, feature evaluation and selection
 
dc.titleAnomaly detection in GPS data based on visual analytics
 
dc.typeConference_Paper
 
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Author Affiliations
  1. University of Illinois at Urbana-Champaign
  2. Chinese Academy of Sciences