A novel complex-events analytical system using episode pattern mining techniques

Jerry C.C. Tseng; Jia Yuan Gu; P. F. Wang; Ching Yu Chen; Vincent Shin-Mu Tseng

doi:10.1007/978-3-319-23862-3_48

A novel complex-events analytical system using episode pattern mining techniques

Jerry C.C. Tseng, Jia Yuan Gu, P. F. Wang, Ching Yu Chen, Vincent Shin-Mu Tseng^*

^*Corresponding author for this work

Institute of Data Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Along with the rapid development of IoT (Internet of Things), there comes the ‘Big Data’ era with the fast growth of digital data and the requirements rise for gaining useful knowledge by analyzing the rich data of complex types. How to effectively and efficiently apply data mining techniques to analyze the big data plays a crucial role in real-world use cases. In this paper, we propose a novel complex-events analytical system based on episode pattern mining techniques. The proposed system consists of four major components, including data preprocessing, pattern mining, rules management and prediction modules. For the core mining process, we proposed a new algorithm named EM-CES (Episode Mining over Complex Event Sequences) based on the sliding window approach. We also make the proposed system integrable with other application platform for complex event analysis, such that users can easily and quickly make use of it to gain the valuable information from complex data. Finally, excellent experimental results on a real-life dataset for electric power consumption monitoring validate the efficiency and effectiveness of the proposed system.

Original language	English
Title of host publication	Intelligence Science and Big Data Engineering
Subtitle of host publication	Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers
Editors	Zhi-Hua Zhou, Baochuan Fu, Fuyuan Hu, Zhancheng Zhang, Zhi-Yong Liu, Yanning Zhang, Xiaofei He, Xinbo Gao
Publisher	Springer Verlag
Pages	487-498
Number of pages	12
ISBN (Print)	9783319238616
DOIs	https://doi.org/10.1007/978-3-319-23862-3_48
State	Published - 1 Jan 2015
Event	5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015 - Suzhou, China Duration: 14 Jun 2015 → 16 Jun 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9243
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015
Country	China
City	Suzhou
Period	14/06/15 → 16/06/15

Keywords

Complex event analytics
Data mining
Episode pattern mining
Multivariate sequence mining

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10.1007/978-3-319-23862-3_48

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Tseng, J. C. C., Gu, J. Y., Wang, P. F., Chen, C. Y., & Tseng, V. S-M. (2015). A novel complex-events analytical system using episode pattern mining techniques. In Z-H. Zhou, B. Fu, F. Hu, Z. Zhang, Z-Y. Liu, Y. Zhang, X. He, & X. Gao (Eds.), Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers (pp. 487-498). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9243). Springer Verlag. https://doi.org/10.1007/978-3-319-23862-3_48

Tseng, Jerry C.C. ; Gu, Jia Yuan ; Wang, P. F. ; Chen, Ching Yu ; Tseng, Vincent Shin-Mu. / A novel complex-events analytical system using episode pattern mining techniques. Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers. editor / Zhi-Hua Zhou ; Baochuan Fu ; Fuyuan Hu ; Zhancheng Zhang ; Zhi-Yong Liu ; Yanning Zhang ; Xiaofei He ; Xinbo Gao. Springer Verlag, 2015. pp. 487-498 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e6ae3d5057f14cd0b5e6bcbdf6126844,

title = "A novel complex-events analytical system using episode pattern mining techniques",

abstract = "Along with the rapid development of IoT (Internet of Things), there comes the {\textquoteleft}Big Data{\textquoteright} era with the fast growth of digital data and the requirements rise for gaining useful knowledge by analyzing the rich data of complex types. How to effectively and efficiently apply data mining techniques to analyze the big data plays a crucial role in real-world use cases. In this paper, we propose a novel complex-events analytical system based on episode pattern mining techniques. The proposed system consists of four major components, including data preprocessing, pattern mining, rules management and prediction modules. For the core mining process, we proposed a new algorithm named EM-CES (Episode Mining over Complex Event Sequences) based on the sliding window approach. We also make the proposed system integrable with other application platform for complex event analysis, such that users can easily and quickly make use of it to gain the valuable information from complex data. Finally, excellent experimental results on a real-life dataset for electric power consumption monitoring validate the efficiency and effectiveness of the proposed system.",

keywords = "Complex event analytics, Data mining, Episode pattern mining, Multivariate sequence mining",

author = "Tseng, {Jerry C.C.} and Gu, {Jia Yuan} and Wang, {P. F.} and Chen, {Ching Yu} and Tseng, {Vincent Shin-Mu}",

year = "2015",

month = jan,

day = "1",

doi = "10.1007/978-3-319-23862-3_48",

language = "English",

isbn = "9783319238616",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "487--498",

editor = "Zhi-Hua Zhou and Baochuan Fu and Fuyuan Hu and Zhancheng Zhang and Zhi-Yong Liu and Yanning Zhang and Xiaofei He and Xinbo Gao",

booktitle = "Intelligence Science and Big Data Engineering",

address = "Germany",

note = "null ; Conference date: 14-06-2015 Through 16-06-2015",

}

Tseng, JCC, Gu, JY, Wang, PF, Chen, CY & Tseng, VS-M 2015, A novel complex-events analytical system using episode pattern mining techniques. in Z-H Zhou, B Fu, F Hu, Z Zhang, Z-Y Liu, Y Zhang, X He & X Gao (eds), Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9243, Springer Verlag, pp. 487-498, 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015, Suzhou, China, 14/06/15. https://doi.org/10.1007/978-3-319-23862-3_48

A novel complex-events analytical system using episode pattern mining techniques. / Tseng, Jerry C.C.; Gu, Jia Yuan; Wang, P. F.; Chen, Ching Yu; Tseng, Vincent Shin-Mu.

Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers. ed. / Zhi-Hua Zhou; Baochuan Fu; Fuyuan Hu; Zhancheng Zhang; Zhi-Yong Liu; Yanning Zhang; Xiaofei He; Xinbo Gao. Springer Verlag, 2015. p. 487-498 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9243).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A novel complex-events analytical system using episode pattern mining techniques

AU - Tseng, Jerry C.C.

AU - Gu, Jia Yuan

AU - Wang, P. F.

AU - Chen, Ching Yu

AU - Tseng, Vincent Shin-Mu

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Along with the rapid development of IoT (Internet of Things), there comes the ‘Big Data’ era with the fast growth of digital data and the requirements rise for gaining useful knowledge by analyzing the rich data of complex types. How to effectively and efficiently apply data mining techniques to analyze the big data plays a crucial role in real-world use cases. In this paper, we propose a novel complex-events analytical system based on episode pattern mining techniques. The proposed system consists of four major components, including data preprocessing, pattern mining, rules management and prediction modules. For the core mining process, we proposed a new algorithm named EM-CES (Episode Mining over Complex Event Sequences) based on the sliding window approach. We also make the proposed system integrable with other application platform for complex event analysis, such that users can easily and quickly make use of it to gain the valuable information from complex data. Finally, excellent experimental results on a real-life dataset for electric power consumption monitoring validate the efficiency and effectiveness of the proposed system.

AB - Along with the rapid development of IoT (Internet of Things), there comes the ‘Big Data’ era with the fast growth of digital data and the requirements rise for gaining useful knowledge by analyzing the rich data of complex types. How to effectively and efficiently apply data mining techniques to analyze the big data plays a crucial role in real-world use cases. In this paper, we propose a novel complex-events analytical system based on episode pattern mining techniques. The proposed system consists of four major components, including data preprocessing, pattern mining, rules management and prediction modules. For the core mining process, we proposed a new algorithm named EM-CES (Episode Mining over Complex Event Sequences) based on the sliding window approach. We also make the proposed system integrable with other application platform for complex event analysis, such that users can easily and quickly make use of it to gain the valuable information from complex data. Finally, excellent experimental results on a real-life dataset for electric power consumption monitoring validate the efficiency and effectiveness of the proposed system.

KW - Complex event analytics

KW - Data mining

KW - Episode pattern mining

KW - Multivariate sequence mining

UR - http://www.scopus.com/inward/record.url?scp=84945973482&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-23862-3_48

DO - 10.1007/978-3-319-23862-3_48

M3 - Conference contribution

AN - SCOPUS:84945973482

SN - 9783319238616

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 487

EP - 498

BT - Intelligence Science and Big Data Engineering

A2 - Zhou, Zhi-Hua

A2 - Fu, Baochuan

A2 - Hu, Fuyuan

A2 - Zhang, Zhancheng

A2 - Liu, Zhi-Yong

A2 - Zhang, Yanning

A2 - He, Xiaofei

A2 - Gao, Xinbo

PB - Springer Verlag

Y2 - 14 June 2015 through 16 June 2015

ER -

Tseng JCC, Gu JY, Wang PF, Chen CY, Tseng VS-M. A novel complex-events analytical system using episode pattern mining techniques. In Zhou Z-H, Fu B, Hu F, Zhang Z, Liu Z-Y, Zhang Y, He X, Gao X, editors, Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers. Springer Verlag. 2015. p. 487-498. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-23862-3_48

A novel complex-events analytical system using episode pattern mining techniques

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