Multi-Source Data Stream Online Frequent Episode Mining

You, Tao; Li, Yamin; Sun, Bingkun; Du, Chenglie

doi:10.1109/access.2020.2997337

Cited by 8 publications

(2 citation statements)

References 31 publications

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“…Hence, the traditional task of FEM is not efficient in such situations. Fortunately, several online and stream episode mining algorithms have been designed (Ao et al, 2015; Cao et al, 2012; Laxman et al, 2008; Li et al, 2018; Narmatha & Shri Hari Aravind, 2016; Patnaik et al, 2012; Qin et al, 2022; Soulas & Lenca, 2015; Tseng et al, 2016; You et al, 2020; Zhongyi et al, 2013; Zhu et al, 2011). Zhu et al have proposed the Extractor algorithm for mining episodes in an event stream and derive prediction rules from them (Zhu et al, 2011).…”

Section: Extensions Of Traditional Episode Miningmentioning

confidence: 99%

A survey of episode mining

Ouarem,

Nouioua,

Fournier‐Viger

2023

WIREs Data Min & Knowl

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Episode mining is a research area in data mining, where the aim is to discover interesting episodes, that is, subsequences of events, in an event sequence. The most popular episode‐mining task is frequent episode mining (FEM), which consists of identifying episodes that appear frequently in an event sequence, but this task has also been extended in various ways. It was shown that episode mining can reveal insightful patterns for numerous applications such as web stream analysis, network fault management, and cybersecurity, and that episodes can be useful for prediction. Episode mining is an active research area, and there have been numerous advances in the field over the last 25 years. However, due to the rapid evolution of the pattern mining field, there is no prior study that summarizes and gives a detailed overview of this field. The contribution of this article is to fill this gap by presenting an up‐to‐date survey that provides an introduction to episode mining and an overview of recent developments and research opportunities. This advanced review first gives an introduction to the field of episode mining and the first algorithms. Then, the main concepts used in these algorithms are explained. After that, several recent studies are reviewed that have addressed some limitations of these algorithms and proposed novel solutions to overcome them. Finally, the paper lists some possible extensions of the existing frameworks to mine more meaningful patterns and presents some possible orientations for future work that may contribute to the evolution of the episode mining field.This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Algorithmic Development > Association Rules Technologies > Association Rules

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Section: Extensions Of Traditional Episode Miningmentioning

confidence: 99%

A survey of episode mining

Ouarem,

Nouioua,

Fournier‐Viger

2023

WIREs Data Min & Knowl

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“…Among data mining techniques, episode mining [2]- [4] is a very powerful tool that can be applied to discover useful patterns from past events to foresee the possible events that will occur in the future. There are many real-life cases around us utilizing this technique, such as manufacturing improvement [5] [6], network attack detection [7], biomedical data analysis [8], high-utility pattern mining [9], news events [10], and stock trend analysis [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

A Scalable Analytical Framework for Complex Event Episode Mining With Various Domains Applications

2022

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With the ubiquity of sensor networks and smart devices that continuously collect data, we face the challenge of analyzing the growing stream of data in real time. In recent years, there has been a huge need to gain useful knowledge by incrementally analyzing event sequence data. Although episode pattern mining techniques have existed for years, people have recently become more aware of their practical value in solving real-life domain problems such as manufacturing records, stock markets, and weather forecasts. The effective and efficient application of episode pattern mining techniques to analyze complex event data is becoming increasingly important for solving real-life problems in wide domains. However, few studies have focused on developing a scalable framework based on episode pattern mining of complex event sequences for applications in various domains. In this work, we propose a novel framework named SAAF (Scalable Analytical Application Framework) based on complex event episode mining techniques, including batch episode mining, delta episode mining, incremental episode mining, and pattern merging, to consider both efficiency and accuracy. Moreover, to enhance scalability, we adopt the lambda architecture with Apache Spark and Apache Spark Streaming as the system development framework. Finally, the experimental results on three real datasets of different domains and two benchmark datasets showed that the proposed SAAF framework exhibits excellent performance in terms of efficiency, accuracy, and scalability.

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