Mining fuzzy time interval sequential pattern on event log data using FP-Growth-Prefix-Span algorithms

Imam Mukhlash; M. A.M.A. Sidratul Muntaha; Mohammad Iqbal; Ahmad Saikhu; Riyanarto Sarno

doi:10.1063/1.4953990

Mining fuzzy time interval sequential pattern on event log data using FP-Growth-Prefix-Span algorithms

Imam Mukhlash^*, M. A.M.A. Sidratul Muntaha, Mohammad Iqbal, Ahmad Saikhu, Riyanarto Sarno

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember

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

2 Citations (Scopus)

Abstract

Rapid technological developments caused the increasing number of computerized data processing. With the increasing complexity of business processes, business process management technologies such as ERP (Enterprise Resource Planning) are increasingly being used. This resulted in the availability of data more abundant so that excavation and search information from the dataset will be a valuable knowledge. In this paper, we have done the process mining to obtain an interesting pattern of event log data. In this research, data mining method that we are used is the sequential pattern mining algorithm using FP-Growth-Prefix Span. In addition, we are also used the fuzzy approach to handle the time interval of the analyzed data, so that the sequential pattern that produced become fuzzy time-interval sequential pattern. The application of these methods in a business processes that produce fuzzy time interval sequential pattern. From the analysis, the result shown that there is a minimum effect on the pattern of the resulting support. Furthermore, the results of the analysis can be used as consideration in the analysis of business processes.

Original language	English
Title of host publication	2016 Conference on Fundamental and Applied Science for Advanced Technology, ConFAST 2016
Subtitle of host publication	Proceeding of ConFAST 2016 Conference Series: International Conference on Physics and Applied Physics Research, ICPR 2016, International Conference on Industrial Biology , ICIBIO 2016, and International Conference on Information System and Applied Mathematics, ICIAMATH 2016
Editors	Rara Sandhy Winanda, Qonitatul Hidayah, Iwan Tri Riyadi Yanto, Nursyiva Irsalinda, Oktira Roka Aji, Damar Yoga Kusuma, Syarifah Inayati
Publisher	American Institute of Physics Inc.
ISBN (Electronic)	9780735414037
DOIs	https://doi.org/10.1063/1.4953990
Publication status	Published - 17 Jun 2016
Event	2016 Conference on Fundamental and Applied Science for Advanced Technology, ConFAST 2016 - Yogyakarta, Indonesia Duration: 25 Jan 2016 → 26 Jan 2016

Publication series

Name	AIP Conference Proceedings
Volume	1746
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	2016 Conference on Fundamental and Applied Science for Advanced Technology, ConFAST 2016
Country/Territory	Indonesia
City	Yogyakarta
Period	25/01/16 → 26/01/16

Keywords

Business processes
FP-growth
Fuzzy time-interval sequential pattern
PrefixSpan
Process mining

Access to Document

10.1063/1.4953990

Cite this

Mukhlash, I., Sidratul Muntaha, M. A. M. A., Iqbal, M., Saikhu, A., & Sarno, R. (2016). Mining fuzzy time interval sequential pattern on event log data using FP-Growth-Prefix-Span algorithms. In R. S. Winanda, Q. Hidayah, I. T. R. Yanto, N. Irsalinda, O. R. Aji, D. Y. Kusuma, & S. Inayati (Eds.), 2016 Conference on Fundamental and Applied Science for Advanced Technology, ConFAST 2016: Proceeding of ConFAST 2016 Conference Series: International Conference on Physics and Applied Physics Research, ICPR 2016, International Conference on Industrial Biology , ICIBIO 2016, and International Conference on Information System and Applied Mathematics, ICIAMATH 2016 Article 020065 (AIP Conference Proceedings; Vol. 1746). American Institute of Physics Inc.. https://doi.org/10.1063/1.4953990

Mukhlash, Imam ; Sidratul Muntaha, M. A.M.A. ; Iqbal, Mohammad et al. / Mining fuzzy time interval sequential pattern on event log data using FP-Growth-Prefix-Span algorithms. 2016 Conference on Fundamental and Applied Science for Advanced Technology, ConFAST 2016: Proceeding of ConFAST 2016 Conference Series: International Conference on Physics and Applied Physics Research, ICPR 2016, International Conference on Industrial Biology , ICIBIO 2016, and International Conference on Information System and Applied Mathematics, ICIAMATH 2016. editor / Rara Sandhy Winanda ; Qonitatul Hidayah ; Iwan Tri Riyadi Yanto ; Nursyiva Irsalinda ; Oktira Roka Aji ; Damar Yoga Kusuma ; Syarifah Inayati. American Institute of Physics Inc., 2016. (AIP Conference Proceedings).

@inproceedings{4d939a49e18c4407839d9bacc840c6b7,

title = "Mining fuzzy time interval sequential pattern on event log data using FP-Growth-Prefix-Span algorithms",

abstract = "Rapid technological developments caused the increasing number of computerized data processing. With the increasing complexity of business processes, business process management technologies such as ERP (Enterprise Resource Planning) are increasingly being used. This resulted in the availability of data more abundant so that excavation and search information from the dataset will be a valuable knowledge. In this paper, we have done the process mining to obtain an interesting pattern of event log data. In this research, data mining method that we are used is the sequential pattern mining algorithm using FP-Growth-Prefix Span. In addition, we are also used the fuzzy approach to handle the time interval of the analyzed data, so that the sequential pattern that produced become fuzzy time-interval sequential pattern. The application of these methods in a business processes that produce fuzzy time interval sequential pattern. From the analysis, the result shown that there is a minimum effect on the pattern of the resulting support. Furthermore, the results of the analysis can be used as consideration in the analysis of business processes.",

keywords = "Business processes, FP-growth, Fuzzy time-interval sequential pattern, PrefixSpan, Process mining",

author = "Imam Mukhlash and {Sidratul Muntaha}, {M. A.M.A.} and Mohammad Iqbal and Ahmad Saikhu and Riyanarto Sarno",

note = "Publisher Copyright: {\textcopyright} 2016 Author(s).; 2016 Conference on Fundamental and Applied Science for Advanced Technology, ConFAST 2016 ; Conference date: 25-01-2016 Through 26-01-2016",

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doi = "10.1063/1.4953990",

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series = "AIP Conference Proceedings",

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booktitle = "2016 Conference on Fundamental and Applied Science for Advanced Technology, ConFAST 2016",

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Mukhlash, I, Sidratul Muntaha, MAMA, Iqbal, M , Saikhu, A & Sarno, R 2016, Mining fuzzy time interval sequential pattern on event log data using FP-Growth-Prefix-Span algorithms. in RS Winanda, Q Hidayah, ITR Yanto, N Irsalinda, OR Aji, DY Kusuma & S Inayati (eds), 2016 Conference on Fundamental and Applied Science for Advanced Technology, ConFAST 2016: Proceeding of ConFAST 2016 Conference Series: International Conference on Physics and Applied Physics Research, ICPR 2016, International Conference on Industrial Biology , ICIBIO 2016, and International Conference on Information System and Applied Mathematics, ICIAMATH 2016., 020065, AIP Conference Proceedings, vol. 1746, American Institute of Physics Inc., 2016 Conference on Fundamental and Applied Science for Advanced Technology, ConFAST 2016, Yogyakarta, Indonesia, 25/01/16. https://doi.org/10.1063/1.4953990

Mining fuzzy time interval sequential pattern on event log data using FP-Growth-Prefix-Span algorithms. / Mukhlash, Imam; Sidratul Muntaha, M. A.M.A.; Iqbal, Mohammad et al.
2016 Conference on Fundamental and Applied Science for Advanced Technology, ConFAST 2016: Proceeding of ConFAST 2016 Conference Series: International Conference on Physics and Applied Physics Research, ICPR 2016, International Conference on Industrial Biology , ICIBIO 2016, and International Conference on Information System and Applied Mathematics, ICIAMATH 2016. ed. / Rara Sandhy Winanda; Qonitatul Hidayah; Iwan Tri Riyadi Yanto; Nursyiva Irsalinda; Oktira Roka Aji; Damar Yoga Kusuma; Syarifah Inayati. American Institute of Physics Inc., 2016. 020065 (AIP Conference Proceedings; Vol. 1746).

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

TY - GEN

T1 - Mining fuzzy time interval sequential pattern on event log data using FP-Growth-Prefix-Span algorithms

AU - Mukhlash, Imam

AU - Sidratul Muntaha, M. A.M.A.

AU - Iqbal, Mohammad

AU - Saikhu, Ahmad

AU - Sarno, Riyanarto

PY - 2016/6/17

Y1 - 2016/6/17

N2 - Rapid technological developments caused the increasing number of computerized data processing. With the increasing complexity of business processes, business process management technologies such as ERP (Enterprise Resource Planning) are increasingly being used. This resulted in the availability of data more abundant so that excavation and search information from the dataset will be a valuable knowledge. In this paper, we have done the process mining to obtain an interesting pattern of event log data. In this research, data mining method that we are used is the sequential pattern mining algorithm using FP-Growth-Prefix Span. In addition, we are also used the fuzzy approach to handle the time interval of the analyzed data, so that the sequential pattern that produced become fuzzy time-interval sequential pattern. The application of these methods in a business processes that produce fuzzy time interval sequential pattern. From the analysis, the result shown that there is a minimum effect on the pattern of the resulting support. Furthermore, the results of the analysis can be used as consideration in the analysis of business processes.

AB - Rapid technological developments caused the increasing number of computerized data processing. With the increasing complexity of business processes, business process management technologies such as ERP (Enterprise Resource Planning) are increasingly being used. This resulted in the availability of data more abundant so that excavation and search information from the dataset will be a valuable knowledge. In this paper, we have done the process mining to obtain an interesting pattern of event log data. In this research, data mining method that we are used is the sequential pattern mining algorithm using FP-Growth-Prefix Span. In addition, we are also used the fuzzy approach to handle the time interval of the analyzed data, so that the sequential pattern that produced become fuzzy time-interval sequential pattern. The application of these methods in a business processes that produce fuzzy time interval sequential pattern. From the analysis, the result shown that there is a minimum effect on the pattern of the resulting support. Furthermore, the results of the analysis can be used as consideration in the analysis of business processes.

KW - Business processes

KW - FP-growth

KW - Fuzzy time-interval sequential pattern

KW - PrefixSpan

KW - Process mining

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DO - 10.1063/1.4953990

M3 - Conference contribution

AN - SCOPUS:84984578933

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BT - 2016 Conference on Fundamental and Applied Science for Advanced Technology, ConFAST 2016

A2 - Winanda, Rara Sandhy

A2 - Hidayah, Qonitatul

A2 - Yanto, Iwan Tri Riyadi

A2 - Irsalinda, Nursyiva

A2 - Aji, Oktira Roka

A2 - Kusuma, Damar Yoga

A2 - Inayati, Syarifah

PB - American Institute of Physics Inc.

T2 - 2016 Conference on Fundamental and Applied Science for Advanced Technology, ConFAST 2016

Y2 - 25 January 2016 through 26 January 2016

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Mukhlash I, Sidratul Muntaha MAMA, Iqbal M , Saikhu A , Sarno R. Mining fuzzy time interval sequential pattern on event log data using FP-Growth-Prefix-Span algorithms. In Winanda RS, Hidayah Q, Yanto ITR, Irsalinda N, Aji OR, Kusuma DY, Inayati S, editors, 2016 Conference on Fundamental and Applied Science for Advanced Technology, ConFAST 2016: Proceeding of ConFAST 2016 Conference Series: International Conference on Physics and Applied Physics Research, ICPR 2016, International Conference on Industrial Biology , ICIBIO 2016, and International Conference on Information System and Applied Mathematics, ICIAMATH 2016. American Institute of Physics Inc. 2016. 020065. (AIP Conference Proceedings). doi: 10.1063/1.4953990

Mining fuzzy time interval sequential pattern on event log data using FP-Growth-Prefix-Span algorithms

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