TY - GEN
T1 - Discovering Instance-Spanning Constraints from Process Execution Logs Based on Classification Techniques
AU - Winter, Karolin
AU - Rinderle-Ma, Stefanie
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/30
Y1 - 2017/10/30
N2 - 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.
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.
KW - Classification Techniques
KW - Constraint Mining
KW - Decision Mining
KW - Instance-Spanning Constraints
UR - http://www.scopus.com/inward/record.url?scp=85046646134&partnerID=8YFLogxK
U2 - 10.1109/EDOC.2017.20
DO - 10.1109/EDOC.2017.20
M3 - Conference contribution
AN - SCOPUS:85046646134
T3 - Proceedings - 2017 IEEE 21st International Enterprise Distributed Object Computing Conference, EDOC 2017
SP - 79
EP - 88
BT - Proceedings - 2017 IEEE 21st International Enterprise Distributed Object Computing Conference, EDOC 2017
A2 - Villemaire, Roger
A2 - Villemaire, Roger
A2 - Lagerstrom, Robert
A2 - Halle, Sylvain
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Enterprise Distributed Object Computing Conference, EDOC 2017
Y2 - 10 October 2017 through 13 October 2017
ER -