Anomaly detection is an important problem that has been researched in several domains. Based on the available data patterns, various supervised and unsupervised anomaly detection techniques have been introduced. In this paper, a novel anomaly detection technique for location aware geospatial big dataset is outlined. Specifically, we focus on anomaly detection in spatiotemporal complex networks. The outlined technique incorporates components of anomaly quantification and decision making on spatiotemporal graphs and embeds simultaneous learning and detection procedures. The magnitude of an anomaly at each time step is quantified to signify the pattern of anomalous behavior in the spatiotemporal network. We illustrate the efficacy of the proposed method by detecting and indicating the time and location of a single or multiple anomalies in an illustrative traffic network problem. Theoretical experiments on a suite of six randomly generated traffic network problems have been performed. The performance of the proposed algorithm with tuned parameters on this random set of problem instances clearly establishes the effectiveness and applicability of the introduced solution procedure.
Skip Nav Destination
ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 21–24, 2016
Charlotte, North Carolina, USA
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-5019-0
PROCEEDINGS PAPER
Anomaly Detection in Complex Spatiotemporal Networks Through Location Aware Geospatial Big Data Sets
Prashant Shekhar,
Prashant Shekhar
University at Buffalo-SUNY, Buffalo, NY
Search for other works by this author on:
Rahul Rai
Rahul Rai
University at Buffalo-SUNY, Buffalo, NY
Search for other works by this author on:
Prashant Shekhar
University at Buffalo-SUNY, Buffalo, NY
Rahul Rai
University at Buffalo-SUNY, Buffalo, NY
Paper No:
DETC2016-59587, V007T06A033; 11 pages
Published Online:
December 5, 2016
Citation
Shekhar, P, & Rai, R. "Anomaly Detection in Complex Spatiotemporal Networks Through Location Aware Geospatial Big Data Sets." Proceedings of the ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 7: 28th International Conference on Design Theory and Methodology. Charlotte, North Carolina, USA. August 21–24, 2016. V007T06A033. ASME. https://doi.org/10.1115/DETC2016-59587
Download citation file:
14
Views
Related Proceedings Papers
Related Articles
Asset Management Evaluation: A Pilot Case Study
J. Pressure Vessel Technol (February,2007)
A Simple Mass-Spring Model With Roller Feet Can Induce the Ground Reactions Observed in Human Walking
J Biomech Eng (January,2009)
Kalman Smoother Based Force Localization and Mapping Using Intravital Video Microscopy
J. Dyn. Sys., Meas., Control (November,2010)
Related Chapters
Distributed Traffic State Estimation and Classification Using Consensus-Based Expectation Maximization Algorithm in Spatially Deployed Traffic Detectors
International Conference on Mechanical Engineering and Technology (ICMET-London 2011)
A New Speed Sign Recognition Algorithm Based on Statistical Characteristics
International Conference on Mechanical Engineering and Technology (ICMET-London 2011)
Securouter — A Novel Dynamic Firewall System Embedded with IDS Integration
International Conference on Computer Technology and Development, 3rd (ICCTD 2011)