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Mining Partners in Trajectories
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Author(s): Diego Vilela Monteiro (INPE, São José dos Campos, Brazil), Rafael Duarte Coelho dos Santos (INPE, São José dos Campos, Brazil)and Karine Reis Ferreira (INPE, São José dos Campos, Brazil)
Copyright: 2020
Volume: 16
Issue: 1
Pages: 17
Source title:
International Journal of Data Warehousing and Mining (IJDWM)
Editor(s)-in-Chief: Eric Pardede (La Trobe University, Australia)and Kiki Adhinugraha (La Trobe University, Australia)
DOI: 10.4018/IJDWM.2020010102
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Abstract
Spatiotemporal data is everywhere, being gathered from different devices such as Earth Observation and GPS satellites, sensor networks and mobile gadgets. Spatiotemporal data collected from moving objects is of particular interest for a broad range of applications. In the last years, such applications have motivated many pieces of research on moving object trajectory data mining. In this article, it is proposed an efficient method to discover partners in moving object trajectories. Such a method identifies pairs of trajectories whose objects stay together during certain periods, based on distance time series analysis. It presents two case studies using the proposed algorithm. This article also describes an R package, called TrajDataMining, that contains algorithms for trajectory data preparation, such as filtering, compressing and clustering, as well as the proposed method Partner.
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