Ranking support for matched patterns over complex event streams: The CEPR system

Gu, Jiaqi; Wang, Jin; Zaniolo, Carlo

doi:10.1109/icde.2016.7498343

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Agrawal et al [2] study pattern matching over event streams. Gu et al [12] explore ranking in pattern matching for complex event streams. Yu et al [35] propose two efficient methods for discovering frequent co-occurrence patterns across multiple data streams.…”

Section: Co-movement Pattern Miningmentioning

confidence: 99%

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Real-time distributed co-movement pattern detection on streaming trajectories

et al. 2019

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With the widespread deployment of mobile devices with positioning capabilities, increasingly massive volumes of trajectory data are being collected that capture the movements of people and vehicles. This data enables co-movement pattern detection, which is important in applications such as trajectory compression and future-movement prediction. Existing co-movement pattern detection studies generally consider historical data and thus propose offline algorithms. However, applications such as future movement prediction need real-time processing over streaming trajectories. Thus, we investigate real-time distributed co-movement pattern detection over streaming trajectories. Existing off-line methods assume that all data is available when the processing starts. Nevertheless, in a streaming setting, unbounded data arrives in real time, making pattern detection challenging. To this end, we propose a framework based on Apache Flink, which is designed for efficient distributed streaming data processing. The framework encompasses two phases: clustering and pattern enumeration. To accelerate the clustering, we use a range join based on two-layer indexing, and provide techniques that eliminate unnecessary verifications. To perform pattern enumeration efficiently, we present two methods FBA and VBA that utilize id-based partitioning. When coupled with bit compression and candidate-based enumeration techniques, we reduce the enumeration cost from exponential to linear. Extensive experiments offer insight into the efficiency of the proposed framework and its constituent techniques compared with existing methods.

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Section: Co-movement Pattern Miningmentioning

confidence: 99%

“…Proceedings of the VLDB Endowment, Vol. 12 data is important in a wide range of applications. One important type of analysis is the discovery of co-moving objects, termed comovement pattern detection.…”

Section: Introductionmentioning

confidence: 99%