Conference Paper: Anomaly detection in GPS data based on visual analytics
| Title | Anomaly detection in GPS data based on visual analytics |
|---|---|
| Authors | Liao, Z1 Yu, Y1 Chen, B2 |
| Keywords | H.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 Date | 2010 |
| Publisher | IEEE. 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?] DOI: http://dx.doi.org/10.1109/VAST.2010.5652467 |
| Abstract | Modern 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. |
| ISBN | 978-1-4244-9487-3 |
| DOI | http://dx.doi.org/10.1109/VAST.2010.5652467 |
| References | References in Scopus |
| dc.contributor.author | Liao, Z |
|---|---|
| dc.contributor.author | Yu, Y |
| dc.contributor.author | Chen, B |
| dc.date.accessioned | 2011-09-23T06:04:35Z |
| dc.date.available | 2011-09-23T06:04:35Z |
| dc.date.issued | 2010 |
| dc.description.abstract | Modern 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.nature | published_or_final_version |
| dc.description.other | 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 |
| dc.identifier.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?] DOI: http://dx.doi.org/10.1109/VAST.2010.5652467 |
| dc.identifier.doi | http://dx.doi.org/10.1109/VAST.2010.5652467 |
| dc.identifier.epage | 58 |
| dc.identifier.hkuros | 194328 |
| dc.identifier.isbn | 978-1-4244-9487-3 |
| dc.identifier.scopus | eid_2-s2.0-78650933347 |
| dc.identifier.spage | 51 |
| dc.identifier.uri | http://hdl.handle.net/10722/140003 |
| dc.language | eng |
| dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001630 |
| dc.relation.ispartof | Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, VAST 2010 |
| dc.relation.references | References in Scopus |
| dc.rights | Creative Commons: Attribution 3.0 Hong Kong License |
| dc.rights | Proceedings 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.subject | H.1.2 [models and principles]: user/Machine systems - human information processing |
| dc.subject | H.5.2 [information interfaces and presentation]: user interfaces - graphics user interfaces |
| dc.subject | I.5.2 [pattern recognition]: design methodology - pattern analysis, feature evaluation and selection |
| dc.title | Anomaly detection in GPS data based on visual analytics |
| dc.type | Conference_Paper |
Author Affiliations
- University of Illinois at Urbana-Champaign
- Chinese Academy of Sciences