A Tour In Process Mining: From Practice to Algorithmic Challenges
Tuesday, June 27, 2017 – Zaragoza, Spain
Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges 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 seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models.
Hence, process mining considers the discovery of process models from real process executions. Discovered models may deviate from reality, and therefore a very important functionality in process mining, as important as discovery, is the automatic assestment of the quality of a process model in representing the reality, a discipline known as conformance checking. Taken together, discovery and conformance checking offer a powerful toolbox to organizations for improving their processes.
Process mining provides not only a bridge between data mining and business process management; it also helps to address the classical divide between “business” and “IT”. Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development.
In the research community related to the topics of the Petri Nets conference, process mining can be the killer application for many other disciplines such as formal methods, concurrency and distributed systems. In particular, the use of Petri nets has grown considerably due being the most popular representation for process mining algorithms.
The motivation of this tutorial is to provide an introductory tour to the field, and then the necessary background and practice so that an attendant can understand the current challenges the field of process mining is facing nowadays. Unlike some of the current textbooks or online courses for process mining, the tutorial will pay special attention to the conformance checking dimension, where some interesting challenges can be addressed.
This tutorial will consists of four modules.
- Module I: A Practical Introduction to Process Mining (1h) – Introduction to process mining with demos of commercial tools (highlighting the incredible relevance and the limitations of existing tools).
- Module II: Discovering Process Models (2h) – Process discovery with inductive mining as an example of scalable discovery technique. Process discovery with region-based approaches.
- Module III: The Challenge of Alignments (1.5h) – Complexity issues for relating observed and modeled behavior. Formal definition of alignments. Selected techniques for the computation of alignments. Alignments applications.
- Module IV: Evidence-based Quality Metrics for Process Models(2h) – Current metrics for measuring the quality of process models with respect to observed behavior. Limitations of some of the discovery approaches.
The following links allow to download the slides and other materials used in the Advanced Tutorial
- Slides of Module I – A Practical Introduction to Process Mining
- Slides of Module II – Discovering Process Models
- Slides of Module III – The Challenge of Alignments
- Slides of Module IV – Evidence-based Quality Metrics for Process Models
- Slides of Simple Metrics
The target audience includes research students as well as researchers. The tutorial is suitable for computer scientists and engineers familiar with basic process modelling approaches. For the case of the last module, the tutorial illustrates in depth the current algorithmic challenges, thus it might be of special interest to those looking for new challenges to their theoretical background.
Resume of the Authors
Wil van der Aalst,
Eindhoven University of Technology, Eindhoven, The Netherlands,
web page: http://wwwis.win.tue.nl/~wvdaalst/
Wil van der Aalst is a full professor of Information Systems at TU/e where he is also the scientific director of the Data Science Center Eindhoven (DSC/e). He is also a member of the Royal Netherlands Academy of Arts and Sciences (Koninklijke Nederlandse Akademie van Wetenschappen), Royal Holland Society of Sciences and Humanities (Koninklijke Hollandsche Maatschappij der Wetenschappen), and the Academy of Europe (Academia Europaea). His personal research interests include process mining, Petri nets, business process management, workflow management, process modeling, and process analysis. He has published more than 200 journal papers, 20 books (as author or editor), 450 refereed conference/workshop publications, and 65 book chapters on these topics.
Universitat Politècnica de Catalunya, Barcelona, Spain.
web page: http://www.cs.upc.edu/~jcarmona/
Josep Carmona is an associate professor at the Department of Computer Science at Universitat Politècnica de Catalunya. In 2004, he received a PhD. in Computer Science from the same university. He is a member of the ALBCOM research group, a multidisciplinary group that holds a distinction from the Government of Catalunya. Furthermore he is a founding member of the IEEE Task Force on Process Mining. Josep published around 80 articles in journals, such as Data Mining and Knowledge Discovery, IEEE TKDE, IEEE Transactions on Computers, Information Systems, and highly competitive conferences like ECML/PKDD, BPM, ATVA, EMNLP, LREC, DAC and ICCAD. He served as PC Co-chair of the ACSD 2011 in Newcastle, and organizes the ATAED workshop since 2011. Josep is the General Chair of the BPM conference in 2017, where he also serves as a PC Co-chair. He co-organizes the Process Discovery Contest.
LSV/ENS Paris-Saclay, Cachan, France.
web page: http://www.lsv.fr/~chatain
Thomas Chatain is an associate professor at ENS Paris-Saclay, France. He received his PhD in 2006 from University of Rennes I (France) and did a postdoc at Aalborg University (Denmark). His research focuses on formal methods for design, verification, control and supervision of distributed and real-time systems.
Boudewijn van Dongen,
Boudewijn van Dongen is an associate professor at the Computer Science department at Eindhoven University of Technology, Eindhoven, The Netherlands. He received his Ph.D. at the Industrial Engineering department of the same university in 2007. Currently, he is a member of the Architecture of Information Systems group which investigates methods, techniques and tools for the design and analysis of process-aware information systems. His research focus is on Process Mining and specifically on conformance checking and since 2003, he has been a key player in the development of the process mining tool ProM. Furthermore, he is a member of the IEEE Task Force on Process Mining and he published extensively in the process mining area, both in international conferences and journals (e.g., DKE, EIS, IS, CAiSE, ATPN, BPM, ER, EDOC). He served in several program committees, among others for IEEE EDOC 2007, 2008, 2009, 2010, BPI 2007 – 2016. Since 2011 he organizes the yearly BPI Challenge, where real-life data is published for researchers and practitioners to show their capabilities in the process mining area.