Madanayake, Adikarige Randil Sanjeewa, Lee, Kyungmi, and Lee, Ickjai (2024) Spatio-Temporal Contact Mining for Multiple Trajectories-of-Interest. IEEE Access, 12. pp. 79458-79467.

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Abstract

Spatio-temporal trajectory is a movement of an object in space over a certain time period, represented by a series of nodes composed of geospatial location and corresponding timestamp. A large amount of spatio-temporal trajectory data is being gathered through various trajectory acquiring devices by tracking the movement of objects such as people, animals, vehicles and natural events. Various trajectory data mining techniques have been proposed to discover useful patterns to understand the behaviour of spatio-temporal trajectories. One unexplored pattern is to identify potential contacts of targeted trajectories which can be defined as contact mining, that is useful for many applications. One such example would be to identify potential victims from known infected humans or animals, especially when the victims are asymptomatic in a rapid spread of infectious disease environments. Another one would be to identify individuals who have been close contacts with known terrorist networks or law breakers. This paper proposes a robust contact mining framework to efficiently and effectively mine contacts of multiple trajectories-of-interest from a given set of spatio-temporal trajectories. Experimental results demonstrate the efficiency, effectiveness and scalability of our approach. In addition, parameter sensitivity analysis reveals the robustness and insensitivity of our framework.

Item ID:	83029
Item Type:	Article (Research - C1)
ISSN:	2169-3536
Copyright Information:	© The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0
Date Deposited:	26 Jun 2024 22:11
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery @ 100%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 100%
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