A real-time frequent pattern mining algorithm for semi structured data streams

Tong, Ziqi; Liao, Husheng; Jin, Xueyun

doi:10.1109/icbda.2017.8078822

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…Typical data stream technology is related to the single-source data stream. Unfortunately, most existing works focus on data stream frequent episode mining, ignoring that the data sources are multi-level, multi-angle and multifaceted, and the traditional single-source data stream mining [17], [18] cannot adapt with social progress. A new sampling system based on binary Bernoulli sampling to support a multi-source data stream environment was made by Wonhyeong Cho et al [15].…”

Section: Related Workmentioning

confidence: 99%

Multi-Source Data Stream Online Frequent Episode Mining

You

Sun

et al. 2020

IEEE Access

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Online frequent episode mining is more complicated than the traditional static frequent episode mining due to the continuous, unbounded and time-varying data stream. Especially in the multiple data streams, online frequent episode mining is more difficult than the single-source stream, due to the concurrency, global clock loss, and uncertainty of delay caused by the distributed environment. To cope with these problems, we propose a new algorithm. Firstly, the data stream with ''happen-before'' relationship among multiple sources is combined on the global data lattice. Next, the traversal on global data lattice generates effective parallel and serial candidate data streams, which guarantee the accuracy of subsequent mining and reduce the number of global sequences during searching process. Then, we use the frequent episode tree to detect the expanding online serial episodes and parallel episodes. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments. INDEX TERMS Episode, global data lattice, multi-source data stream.

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Section: Related Workmentioning

confidence: 99%