TY - JOUR
T1 - From low-level-event-logs to high-level-business-process-model-activities
T2 - 29th Italian Symposium on Advanced Database Systems, SEBD 2021
AU - Cuzzocrea, Alfredo
AU - Damiani, Ernesto
AU - Al-Ali, Hamda
AU - Mizouni, Rabeb
AU - Tello, Ghalia
AU - Fadda, Edoardo
N1 - Publisher Copyright:
© 2021 Copyright for this paper by its authors.
PY - 2021
Y1 - 2021
N2 - Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine learning-based classification. In automatic labeling a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase.
AB - Process mining is an emerging discipline that aims to analyze business processes using event data logged by IT systems. In process mining, the focus is on how to effectively and efficiently predict the next process/trace to be activated among all the possible processes/traces that are available in the process schema (usually modeled as a graph). Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and the events that are recorded during process execution. However, event logs and process model activities are at different level of granularity. In this paper, we present a machine learning-based approach to map low-level event logs to high-level activities. With this work, we can bridge the abstraction levels when the high-level labels of the low-level events are not available. The proposed approach consists of two main phases: automatic labeling and machine learning-based classification. In automatic labeling a modified k-prototypes clustering approach has been used in order to obtain the labeled examples. Then, in the second phase, we trained different ML classifiers using the obtained labeled examples. Since, in real-life applications and systems, business processes are expressed according to the Business Process Model and Notation (BPMN) format, we improve our proposed framework by means of an innovative, flexible BPMN model translation methodology that acts at the first phase.
KW - BPMN model translation
KW - Business process management
KW - Business process mining
UR - http://www.scopus.com/inward/record.url?scp=85118809205&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85118809205
SN - 1613-0073
VL - 2994
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 5 September 2021 through 9 September 2021
ER -