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Conference Paper: Anomaly detection in GPS data based on visual analytics

TitleAnomaly detection in GPS data based on visual analytics
Authors
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
Citation
The 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?
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.
Persistent Identifierhttp://hdl.handle.net/10722/140003
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorLiao, Zen_HK
dc.contributor.authorYu, Yen_HK
dc.contributor.authorChen, Ben_HK
dc.date.accessioned2011-09-23T06:04:35Z-
dc.date.available2011-09-23T06:04:35Z-
dc.date.issued2010en_HK
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. 51-58en_HK
dc.identifier.isbn978-1-4244-9487-3-
dc.identifier.urihttp://hdl.handle.net/10722/140003-
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.en_HK
dc.languageengen_US
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 2010en_HK
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 processingen_HK
dc.subjectH.5.2 [information interfaces and presentation]: user interfaces - graphics user interfacesen_HK
dc.subjectI.5.2 [pattern recognition]: design methodology - pattern analysis, feature evaluation and selectionen_HK
dc.titleAnomaly detection in GPS data based on visual analyticsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailYu, Y:yzyu@cs.hku.hken_HK
dc.identifier.authorityYu, Y=rp01415en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/VAST.2010.5652467en_HK
dc.identifier.scopuseid_2-s2.0-78650933347en_HK
dc.identifier.hkuros194328en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78650933347&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage51en_HK
dc.identifier.epage58en_HK
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.scopusauthoridLiao, Z=36727776600en_HK
dc.identifier.scopusauthoridYu, Y=8554163500en_HK
dc.identifier.scopusauthoridChen, B=36833474700en_HK

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