Abstract
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.
Highlights
Data stream frequent episode mining techniques are broadly used in network security monitoring analyzing [1], financial securities management [2], mobile communication services [3], and sensor data processing [4]
RELATED WORK We study the problem of multi-source data stream online frequent episode mining, there are several kinds of research related to this work, including multi-source data stream combination and online frequent episode mining
It is easy to see from the tree construction process that each time a new frequent episode is mined, a node is added to the Frequent Episode Tree (FET)
Summary
Data stream frequent episode mining techniques are broadly used in network security monitoring analyzing [1], financial securities management [2], mobile communication services [3], and sensor data processing [4]. Implementing a multi-source data stream can overcome the limitations of the single-source data stream, and combine to filter out interference information to form a comprehensive decision on the results, which can produce more accurate, reliable, and effective data compared with the single-source data Another application is the wireless sensor network scenario [11], [12]. We propose an online frequent episode mining algorithm for the multi-source data stream based on order features. It is the first time that the research of multi-source data stream online frequent episode mining is proposed.
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