Context-aware Adaptive Outlier Detection in Trajectory Data

Danda, Srinivas and Zhang, Ji ORCID: https://orcid.org/0000-0001-7167-6970 and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Chun-Wei, Jerry and Zhang, Wenbin (2020) Context-aware Adaptive Outlier Detection in Trajectory Data. In: 8th IEEE International Conference on Big Data (2020), 10 Dec - 13 Dec 2020, Atlanta, United States.


Abstract

With the advent of data mining and business processes automation, outlier detection has evolved into a major problem attracting significant research in relation to several application domains. Further advances in Global Positioning system, tracking of anomalous events based on data enhances effective decision making and pro-active measures to overcome risks and avoid unwarranted outputs. Significant work has been done in trajectory outlier detection although no singular approach fits all the domains. By including position and collective outliers on the same visualizations will enhance understanding of an outlier behavior. As such, we have leveraged Hidden Markov Method for prediction-based point outlier detection and pattern mining to identify points or segments of outliers in trajectory data.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021)
Date Deposited: 12 May 2022 23:42
Last Modified: 30 May 2022 03:17
Uncontrolled Keywords: Context-aware, Adaptive Outlier Detection, Trajectory Data
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460501 Data engineering and data science
46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence
Identification Number or DOI: https://doi.org/10.1109/BigData50022.2020.9378046
URI: http://eprints.usq.edu.au/id/eprint/48463

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