Garden: a real-time processing framework for continuous top-k trajectory similarity search

Pan, Zhicheng; Chao, Pingfu; Fang, Junhua; Chen, Wei; Xu, Jiajie; Zhao, Lei

doi:10.1007/s10115-023-01880-z

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…At the same time, it also implemented spatio-temporal partitioning and several classic spatio-temporal queries. Based on Flink, Pan et al [27] introduced a state reuse mechanism and an index-based pruning method to achieve continuous top-k trajectory similarity search. This move greatly reduces the computational cost of trajectory similarity search.…”

Section: Flink-based Traffic Applicationsmentioning

confidence: 99%

Research on Real-Time Anomaly Detection Method of Bus Trajectory Based on Flink

Zou,

Xiong,

Wang

et al. 2023

Electronics

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Bus transportation system has become the primary mode of traffic for urban residents. Every day, thousands of buses provide services for millions of passengers. Efficiently monitoring bus trajectories is essential for evaluating service quality and ensuring public safety. In this study, we propose a Flink-based solution to detect anomalies for bus trajectories in real time. Specifically, it can identify two types of anomalies. The first type is when a bus deviates from its designated route during a trip. The second type is when a bus arrives at a scheduled stop along its route but fails to stop. This solution employs CEP (Complex Event Processing) to determine bus arrival events and control the detection process. In this process, it utilizes the state management mechanism to save and update a bus’s actual trajectory, which is derived from the raw GPS trajectory and maintained as a stop sequence. Subsequently, it uses LCSS (Longest Common Subsequence) to measure the trajectory similarity between the actual bus trajectory and the scheduled route. We validate the solution using a large-scale real dataset in a Flink cluster with six virtual machines. The experimental results show that (1) each core can handle anomaly detection on 12.5 buses simultaneously and (2) the detection accuracies of the two anomalies are 90.5% and 89.3%, respectively.

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Section: Flink-based Traffic Applicationsmentioning

confidence: 99%

Research on Real-Time Anomaly Detection Method of Bus Trajectory Based on Flink

Zou,

Xiong,

Wang

et al. 2023

Electronics

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“…Numerous researchers have studied the problem of k-similarity trajectories search [14][15][16][17][18]. However, a major portion of these endeavors have primarily concentrated on addressing k-similarity trajectories search over static or historical trajectory datasets.…”

Section: Introductionmentioning

confidence: 99%

RNDLP: A Distributed Framework for Supporting Continuous k-Similarity Trajectories Search over Road Network

Jiang,

Tong,

Zhu

et al. 2024

Mathematics

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Continuous k-similarity trajectories search over a data stream is an important problem in the domain of spatio-temporal databases. Given a set of trajectories T and a query trajectory Tq over road network G, the system monitors trajectories within T, reporting k trajectories that are the most similar to Tq whenever one time unit is passed. Some existing works study k-similarity trajectories search over trajectory data, but they cannot work in a road network environment, especially when the trajectory set scale is large. In this paper, we propose a novel framework named RNDLP (Road Network-based Distance Lower-bound-based Prediction) to support CKTRN over trajectory data. It is a distributed framework based on the following observation. That is, given a trajectory Ti and the query trajectory Tq, when we have knowledge of D(Ti), we can compute the lower-bound and upper-bound distances between Tq and Ti, which enables us to predict the scores of trajectories in T and employ these predictions to assess the significance of trajectories within T. Accordingly, we can form a mathematical model to evaluate the excepted running cost of each trajectory we should spend. Based on the model, we propose a partition algorithm to partition trajectories into a group of servers so as to guarantee that the workload of each server is as the same as possible. In each server, we propose a pair-based algorithm to predict the earliest time Ti could become a query result, and use the predicted result to organize these trajectories. Our proposed algorithm helps us support query processing via accessing a few points of a small number of trajectories whenever trajectories are updated. Finally, we conduct extensive performance studies on large, real, and synthetic datasets, which demonstrate that our new framework could efficiently support CKST over a data stream.

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