Parallel grid-based density peak clustering of big trajectory data

Niu, Xinjian; Zheng, Yunhong; Fournier‐Viger, Philippe; Wang, Bing

doi:10.1007/s10489-021-02757-w

Cited by 5 publications

(1 citation statement)

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“…For example, Gudmundsson and Horton (2017) provided a thorough coverage on the use of trajectories in sports sciences, and specifically in invasion sports such as football or basketball. Similarly, Niu et al (2021) stated that streaming trajectory, incremental trajectory or parallel trajectory are all used to address research conducted on a distributed environment. Finally, researchers may not have explicitly emphasised the trajectory keyword, and only used 'big data' in their papers, which may also lead to the omission of relevant papers.…”

Section: Discussionmentioning

confidence: 99%

Big Trajectory Data: A Distributed Computing Perspective

Anbaroğlu

Alter

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Trajectory data constitute location of objects at specified time intervals. The continuous availability of GNSS signals, or discrete availability of sensor systems such as license plate recognition cameras are used to generate trajectory data. Consequently, in a smart city context, big trajectory data are being generated on a daily basis. The analysis of big trajectory data entails the use of a distributed environment to conduct analysis, and at least two data sources. The literature review conducted in this paper shows that the two Vs of big data, Volume and Variety, may not be satisfied since researchers usually rely on a centralised computing environment, and analyse data coming from a single data source. Out of the 17 papers published from 2020 in Scopus, only five of them relied on a distributed computing environment, and two of them utilised more than one data source.

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Section: Discussionmentioning

confidence: 99%