Parallel Discovery of Trajectory Companions from Heterogeneous Streaming Data

Xian, Yongyi; Xu, Chuanfei; Elnikety, Sameh; Liu, Yan

doi:10.1109/compsac.2019.00071

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…We analyze polyline distance relations in each slot. In the d-dimensional space, the polyline of object o i passes the points <(x (1) 1 , ...,…”

Section: Polyline-to-polyline Approachmentioning

confidence: 99%

“…Our previous work in Ref. [1] targets at the first two research questions and discovers relations of trajectory objects at continous timestamps. We develop a trajectory slot model for compute trajectory companion with objects' locations at each timestamp and objects' movements in a time period.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is an extension to Ref. [1] specifically to investigate the third research question. Our trajectory slot model requires intersection of trajectory objects from partitions of trajectory streams.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parallel Discovery of Trajectory Companion Pattern and System Evaluation

Xian¹,

Liu²,

Xu³

et al. 2020

Journal of Information Processing

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Trajectories consist of spatial information of moving objects. Over contious time spans, trajectory data form data streams constantly generated from diverse and geographically distributed sources. Discovery of traveling patterns on trajectory streams such as gathering and companies enables value domain applications. Such a discovery needs to process arrival records in various sources and correlate across records near real-time. Thus techniques for handling trajectory streams should scale on distributed cluster computing. The challenge is at three aspects, namely a data model to represent the continuous trajectory data, the parallelism of the discovery algorithm, and an end-to-end parallel framework. In this paper, we propose a parallel discovery method that consists of 1) a model of partitioning trajectory samples on various time intervals; 2) definition on distance measurements of trajectories; and 3) a parallel discovery algorithm. We build a stream processing workflow and investigate experiments on a public dataset to evaluate the system's performance, scalability, stability, and data intensity. Our method discovers trajectory gathering patterns with low latency and scales as the size of trajectory data grows.

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“…We analyze polyline distance relations in each slot. In the d-dimensional space, the polyline of object o i passes the points <(x (1) 1 , ...,…”

Section: Polyline-to-polyline Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%