Discovering Instance-Spanning Constraints from Process Execution Logs Based on Classification Techniques

Karolin Winter; Stefanie Rinderle-Ma

doi:10.1109/EDOC.2017.20

Discovering Instance-Spanning Constraints from Process Execution Logs Based on Classification Techniques

Karolin Winter, Stefanie Rinderle-Ma

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

8 Citations (Scopus)

Abstract

Process-aware Information Systems (PAIS) have become ubiquitous in companies. Thus the amount of data that can be used to analyze and monitor process executions is vast. The event logs generated by PAIS might contain information about decision making processes and can support the understanding and improving of procedures in companies. Mining decisions and constraints from logs has already been investigated, but so far only for each instance in a separate manner. However, in many practical settings instances are connected to each other if they share, for example, the same resources. Therefore, we present an approach for discovering Instance-Spanning Constraints (ISC) from event logs. The main idea is to identify instance-spanning attributes in the logs and to separate the logs accordingly. Based on these projections, classification algorithms are applied in order to obtain ISC candidates. The feasibility and applicability of the approach is evaluated based on artificial as well as real-life logs. The discovered ISC candidates are then assessed by domain experts.

Original language	English
Title of host publication	Proceedings - 2017 IEEE 21st International Enterprise Distributed Object Computing Conference, EDOC 2017
Editors	Roger Villemaire, Robert Lagerstrom, Sylvain Halle
Publisher	Institute of Electrical and Electronics Engineers
Pages	79-88
Number of pages	10
ISBN (Electronic)	9781509030453
DOIs	https://doi.org/10.1109/EDOC.2017.20
Publication status	Published - 2 Nov 2017
Externally published	Yes
Event	21st IEEE International Enterprise Distributed Object Computing Conference, EDOC 2017 - Quebec City, Canada Duration: 10 Oct 2017 → 13 Oct 2017 Conference number: 21

Conference

Conference	21st IEEE International Enterprise Distributed Object Computing Conference, EDOC 2017
Abbreviated title	EDOC 2017
Country/Territory	Canada
City	Quebec City
Period	10/10/17 → 13/10/17

Funding

ACKNOWLEDGMENT This work has been funded by the Vienna Science and TechnologyFund (WWTF) through project ICT15-072.

Keywords

Classification Techniques
Constraint Mining
Decision Mining
Instance-Spanning Constraints

Access to Document

10.1109/EDOC.2017.20

Cite this

Winter, K., & Rinderle-Ma, S. (2017). Discovering Instance-Spanning Constraints from Process Execution Logs Based on Classification Techniques. In R. Villemaire, R. Lagerstrom, & S. Halle (Eds.), Proceedings - 2017 IEEE 21st International Enterprise Distributed Object Computing Conference, EDOC 2017 (pp. 79-88). Article 8089866 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EDOC.2017.20

Winter, Karolin ; Rinderle-Ma, Stefanie. / Discovering Instance-Spanning Constraints from Process Execution Logs Based on Classification Techniques. Proceedings - 2017 IEEE 21st International Enterprise Distributed Object Computing Conference, EDOC 2017. editor / Roger Villemaire ; Robert Lagerstrom ; Sylvain Halle. Institute of Electrical and Electronics Engineers, 2017. pp. 79-88

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title = "Discovering Instance-Spanning Constraints from Process Execution Logs Based on Classification Techniques",

abstract = "Process-aware Information Systems (PAIS) have become ubiquitous in companies. Thus the amount of data that can be used to analyze and monitor process executions is vast. The event logs generated by PAIS might contain information about decision making processes and can support the understanding and improving of procedures in companies. Mining decisions and constraints from logs has already been investigated, but so far only for each instance in a separate manner. However, in many practical settings instances are connected to each other if they share, for example, the same resources. Therefore, we present an approach for discovering Instance-Spanning Constraints (ISC) from event logs. The main idea is to identify instance-spanning attributes in the logs and to separate the logs accordingly. Based on these projections, classification algorithms are applied in order to obtain ISC candidates. The feasibility and applicability of the approach is evaluated based on artificial as well as real-life logs. The discovered ISC candidates are then assessed by domain experts.",

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Winter, K & Rinderle-Ma, S 2017, Discovering Instance-Spanning Constraints from Process Execution Logs Based on Classification Techniques. in R Villemaire, R Lagerstrom & S Halle (eds), Proceedings - 2017 IEEE 21st International Enterprise Distributed Object Computing Conference, EDOC 2017., 8089866, Institute of Electrical and Electronics Engineers, pp. 79-88, 21st IEEE International Enterprise Distributed Object Computing Conference, EDOC 2017, Quebec City, Quebec, Canada, 10/10/17. https://doi.org/10.1109/EDOC.2017.20

Discovering Instance-Spanning Constraints from Process Execution Logs Based on Classification Techniques. / Winter, Karolin; Rinderle-Ma, Stefanie.
Proceedings - 2017 IEEE 21st International Enterprise Distributed Object Computing Conference, EDOC 2017. ed. / Roger Villemaire; Robert Lagerstrom; Sylvain Halle. Institute of Electrical and Electronics Engineers, 2017. p. 79-88 8089866.

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

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AU - Rinderle-Ma, Stefanie

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Y1 - 2017/11/2

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AB - Process-aware Information Systems (PAIS) have become ubiquitous in companies. Thus the amount of data that can be used to analyze and monitor process executions is vast. The event logs generated by PAIS might contain information about decision making processes and can support the understanding and improving of procedures in companies. Mining decisions and constraints from logs has already been investigated, but so far only for each instance in a separate manner. However, in many practical settings instances are connected to each other if they share, for example, the same resources. Therefore, we present an approach for discovering Instance-Spanning Constraints (ISC) from event logs. The main idea is to identify instance-spanning attributes in the logs and to separate the logs accordingly. Based on these projections, classification algorithms are applied in order to obtain ISC candidates. The feasibility and applicability of the approach is evaluated based on artificial as well as real-life logs. The discovered ISC candidates are then assessed by domain experts.

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Winter K, Rinderle-Ma S. Discovering Instance-Spanning Constraints from Process Execution Logs Based on Classification Techniques. In Villemaire R, Lagerstrom R, Halle S, editors, Proceedings - 2017 IEEE 21st International Enterprise Distributed Object Computing Conference, EDOC 2017. Institute of Electrical and Electronics Engineers. 2017. p. 79-88. 8089866 doi: 10.1109/EDOC.2017.20

Discovering Instance-Spanning Constraints from Process Execution Logs Based on Classification Techniques

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