TY - GEN
T1 - A tour in process mining
T2 - 39th International Conference on Application and Theory of Petri Nets and Concurrency, Petri Nets 2018, and the 18th International Conference on Application of Concurrency to System Design, ACSD 2018
AU - van der Aalst, Wil
AU - Carmona, Josep
AU - Chatain, Thomas
AU - van Dongen, Boudewijn
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Process mining seeks the confrontation between modeled behavior and observed behavior. In recent years, process mining techniques managed to bridge the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining is used by many data-driven organizations as a means to improve performance or to ensure compliance. Traditionally, the focus was on the discovery of process models from event logs describing real process executions. However, process mining is not limited to process discovery and also includes conformance checking. Process models (discovered or hand-made) may deviate from reality. Therefore, we need powerful means to analyze discrepancies between models and logs. These are provided by conformance checking techniques that first align modeled and observed behavior, and then compare both. The resulting alignments are also used to enrich process models with performance related information extracted from the event log. This tutorial paper focuses on the control-flow perspective and describes a range of process discovery and conformance checking techniques. The goal of the paper is to show the algorithmic challenges in process mining. We will show that process mining provides a wealth of opportunities for people doing research on Petri nets and related models of concurrency.
AB - Process mining seeks the confrontation between modeled behavior and observed behavior. In recent years, process mining techniques managed to bridge the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining is used by many data-driven organizations as a means to improve performance or to ensure compliance. Traditionally, the focus was on the discovery of process models from event logs describing real process executions. However, process mining is not limited to process discovery and also includes conformance checking. Process models (discovered or hand-made) may deviate from reality. Therefore, we need powerful means to analyze discrepancies between models and logs. These are provided by conformance checking techniques that first align modeled and observed behavior, and then compare both. The resulting alignments are also used to enrich process models with performance related information extracted from the event log. This tutorial paper focuses on the control-flow perspective and describes a range of process discovery and conformance checking techniques. The goal of the paper is to show the algorithmic challenges in process mining. We will show that process mining provides a wealth of opportunities for people doing research on Petri nets and related models of concurrency.
UR - http://www.scopus.com/inward/record.url?scp=85076488855&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-60651-3_1
DO - 10.1007/978-3-662-60651-3_1
M3 - Conference contribution
AN - SCOPUS:85076488855
SN - 9783662606506
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 35
BT - Transactions on Petri Nets and Other Models of Concurrency XIV
A2 - Koutny, Maciej
A2 - Pomello, Lucia
A2 - Kristensen, Lars Michael
PB - Springer
Y2 - 24 June 2019 through 29 June 2019
ER -