Model discovery of parallel business processes using modified Heuristic Miner

Riyanarto Sarno; Fitrianing Haryadita; Dwi Sunaryono; Abdul Munif

doi:10.1109/ICSITech.2015.7407772

Model discovery of parallel business processes using modified Heuristic Miner

Riyanarto Sarno^*, Fitrianing Haryadita, Dwi Sunaryono, Abdul Munif

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember

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

5 Citations (Scopus)

Abstract

Process Mining or Process Discovery is a method to automatically discover process models from event log data. Since the process discovery is gaining attention among researchers as well as practitioners, the quality of the resulted process models is required. Business process model contains sequence and parallel traces. Many algorithms have been employed for process discovery, such as Alpha, Alpha++ and Heuristic Miner. Both Alpha ++ and existing Heuristic Miner cannot discover processes containing parallel OR. In this paper we propose the modified Heuristic Miner which utilizes the threshold intervals to discover parallel XOR, AND, and OR. The threshold intervals are determined based on average dependency measure in dependency graph. The results show that the modified Heuristic Miner can discover OR split and join which cannot be discovered by Alpha ++ as well as the existing Heuristic Miner.

Original language	English
Title of host publication	Proceedings - 2015 International Conference on Science in Information Technology
Subtitle of host publication	Big Data Spectrum for Future Information Economy, ICSITech 2015
Editors	Yana Hendriana, Andri Pranolo, Adhi Prahara, Dewi Pramudi Ismi
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	30-35
Number of pages	6
ISBN (Electronic)	9781479983865
DOIs	https://doi.org/10.1109/ICSITech.2015.7407772
Publication status	Published - 16 Feb 2016
Event	International Conference on Science in Information Technology, ICSITech 2015 - Yogyakarta, Indonesia Duration: 27 Oct 2015 → 28 Oct 2015

Publication series

Name	Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015

Conference

Conference	International Conference on Science in Information Technology, ICSITech 2015
Country/Territory	Indonesia
City	Yogyakarta
Period	27/10/15 → 28/10/15

Keywords

Discovery Parallel Activity OR and AND
Modified Heuristic Miner
Process Discovery

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10.1109/ICSITech.2015.7407772

Cite this

Sarno, R., Haryadita, F., Sunaryono, D., & Munif, A. (2016). Model discovery of parallel business processes using modified Heuristic Miner. In Y. Hendriana, A. Pranolo, A. Prahara, & D. P. Ismi (Eds.), Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015 (pp. 30-35). Article 7407772 (Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSITech.2015.7407772

Sarno, Riyanarto ; Haryadita, Fitrianing ; Sunaryono, Dwi et al. / Model discovery of parallel business processes using modified Heuristic Miner. Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015. editor / Yana Hendriana ; Andri Pranolo ; Adhi Prahara ; Dewi Pramudi Ismi. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 30-35 (Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015).

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title = "Model discovery of parallel business processes using modified Heuristic Miner",

abstract = "Process Mining or Process Discovery is a method to automatically discover process models from event log data. Since the process discovery is gaining attention among researchers as well as practitioners, the quality of the resulted process models is required. Business process model contains sequence and parallel traces. Many algorithms have been employed for process discovery, such as Alpha, Alpha++ and Heuristic Miner. Both Alpha ++ and existing Heuristic Miner cannot discover processes containing parallel OR. In this paper we propose the modified Heuristic Miner which utilizes the threshold intervals to discover parallel XOR, AND, and OR. The threshold intervals are determined based on average dependency measure in dependency graph. The results show that the modified Heuristic Miner can discover OR split and join which cannot be discovered by Alpha ++ as well as the existing Heuristic Miner.",

keywords = "Discovery Parallel Activity OR and AND, Modified Heuristic Miner, Process Discovery",

author = "Riyanarto Sarno and Fitrianing Haryadita and Dwi Sunaryono and Abdul Munif",

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doi = "10.1109/ICSITech.2015.7407772",

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Sarno, R, Haryadita, F, Sunaryono, D & Munif, A 2016, Model discovery of parallel business processes using modified Heuristic Miner. in Y Hendriana, A Pranolo, A Prahara & DP Ismi (eds), Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015., 7407772, Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015, Institute of Electrical and Electronics Engineers Inc., pp. 30-35, International Conference on Science in Information Technology, ICSITech 2015, Yogyakarta, Indonesia, 27/10/15. https://doi.org/10.1109/ICSITech.2015.7407772

Model discovery of parallel business processes using modified Heuristic Miner. / Sarno, Riyanarto; Haryadita, Fitrianing; Sunaryono, Dwi et al.
Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015. ed. / Yana Hendriana; Andri Pranolo; Adhi Prahara; Dewi Pramudi Ismi. Institute of Electrical and Electronics Engineers Inc., 2016. p. 30-35 7407772 (Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015).

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

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AU - Sarno, Riyanarto

AU - Haryadita, Fitrianing

AU - Sunaryono, Dwi

AU - Munif, Abdul

PY - 2016/2/16

Y1 - 2016/2/16

N2 - Process Mining or Process Discovery is a method to automatically discover process models from event log data. Since the process discovery is gaining attention among researchers as well as practitioners, the quality of the resulted process models is required. Business process model contains sequence and parallel traces. Many algorithms have been employed for process discovery, such as Alpha, Alpha++ and Heuristic Miner. Both Alpha ++ and existing Heuristic Miner cannot discover processes containing parallel OR. In this paper we propose the modified Heuristic Miner which utilizes the threshold intervals to discover parallel XOR, AND, and OR. The threshold intervals are determined based on average dependency measure in dependency graph. The results show that the modified Heuristic Miner can discover OR split and join which cannot be discovered by Alpha ++ as well as the existing Heuristic Miner.

AB - Process Mining or Process Discovery is a method to automatically discover process models from event log data. Since the process discovery is gaining attention among researchers as well as practitioners, the quality of the resulted process models is required. Business process model contains sequence and parallel traces. Many algorithms have been employed for process discovery, such as Alpha, Alpha++ and Heuristic Miner. Both Alpha ++ and existing Heuristic Miner cannot discover processes containing parallel OR. In this paper we propose the modified Heuristic Miner which utilizes the threshold intervals to discover parallel XOR, AND, and OR. The threshold intervals are determined based on average dependency measure in dependency graph. The results show that the modified Heuristic Miner can discover OR split and join which cannot be discovered by Alpha ++ as well as the existing Heuristic Miner.

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Sarno R, Haryadita F, Sunaryono D , Munif A. Model discovery of parallel business processes using modified Heuristic Miner. In Hendriana Y, Pranolo A, Prahara A, Ismi DP, editors, Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 30-35. 7407772. (Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015). doi: 10.1109/ICSITech.2015.7407772

Model discovery of parallel business processes using modified Heuristic Miner

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