A Scalable Analytical Framework for Complex Event Episode Mining With Various Domains Applications Article Swipe
J. Tseng
,
Sun‐Yuan Hsieh
,
Vincent S. Tseng
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/access.2022.3228962
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/access.2022.3228962
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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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2022.3228962
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/09982636.pdf
- OA Status
- gold
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312478493
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312478493Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2022.3228962Digital Object Identifier
- Title
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A Scalable Analytical Framework for Complex Event Episode Mining With Various Domains ApplicationsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
J. Tseng, Sun‐Yuan Hsieh, Vincent S. TsengList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2022.3228962Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/09982636.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/09982636.pdfDirect OA link when available
- Concepts
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Scalability, Computer science, SPARK (programming language), Event (particle physics), Data mining, Data stream mining, Big data, Machine learning, Artificial intelligence, Data science, Database, Programming language, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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57Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6735928231, https://openalex.org/W2156026066, https://openalex.org/W6602383291, https://openalex.org/W6600858539, https://openalex.org/W2133535525, https://openalex.org/W3030304602, https://openalex.org/W1993690978, https://openalex.org/W1594479771, https://openalex.org/W1971697515, https://openalex.org/W2803386330, https://openalex.org/W2151434516, https://openalex.org/W6714380846, https://openalex.org/W4253573770, https://openalex.org/W2114624736, https://openalex.org/W2052360255, https://openalex.org/W2138730049, https://openalex.org/W2035412162, https://openalex.org/W2169354187, https://openalex.org/W2152671886, https://openalex.org/W410850256, https://openalex.org/W2530314904, https://openalex.org/W3017025919, https://openalex.org/W3004792188, https://openalex.org/W2399436387, https://openalex.org/W2557741071, https://openalex.org/W2033727591, https://openalex.org/W1524489222, https://openalex.org/W2003957650, https://openalex.org/W4288598360, https://openalex.org/W2999363993, https://openalex.org/W1525412602, https://openalex.org/W2158545946, https://openalex.org/W2921411346, https://openalex.org/W2800017158, https://openalex.org/W2762334571, https://openalex.org/W2972489831, https://openalex.org/W2799775760, https://openalex.org/W2952450397, https://openalex.org/W2988005020, https://openalex.org/W2102936290, https://openalex.org/W2069980026, https://openalex.org/W2051292379, https://openalex.org/W2963212997, https://openalex.org/W3183930927, https://openalex.org/W1989037929, https://openalex.org/W3197066296, https://openalex.org/W4210496617, https://openalex.org/W6809851174, https://openalex.org/W3026889840, https://openalex.org/W2119738171, https://openalex.org/W2147694185, https://openalex.org/W2173213060, https://openalex.org/W2168196587, https://openalex.org/W2991050497, https://openalex.org/W2408401645, https://openalex.org/W3037839749, https://openalex.org/W4221151704 |
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