18th Int. Conference on Business Process Management (BPM 2020)

Tutorials

BPM 2020 will offer five tutorials as part of the main conference (details below):

Title: Information Systems Modeling: Playing with the Interplay Between Data and Processes

Abstract: Data and processes go hand-in-hand in information systems but are often modeled, validated, and verified separately in the systems’ design phases. Designers of information systems often proceed by ensuring that database tables satisfy normal forms, and process models capturing the dynamics of the intended information manipulations are deadlock and livelock free. However, such an approach is not sufficient, as perfect data and process designs assessed in isolation can, indeed, induce faults when combined in the end system.

In this tutorial, we demonstrate our recent approach to modeling and verification of models of information systems in three parts. Firstly, we present our Information Systems Modeling Language (ISML) for describing information and process constraints and the interplay between these two types of constraints. Secondly, we demonstrate Information Systems Modeling Suite (ISM Suite), an integrated environment for developing, simulating, and analyzing models of information systems described in ISML, released under an open-source license. In this part, using our tools, we show several example pitfalls at the level of information and process interplay. Finally, we discuss current and future research directions that aim at strengthening the theoretical foundations and practical aspects of our approach to the design of information systems.

Authors:

  • Artem Polyvyanyy (University of Melbourne, Australia)
  • Jan Martijn van der Werf (Utrecht University, The Netherlands)

Authors Bio:

Dr. Artem Polyvyanyy is a senior lecturer at the School of Computing and Information Systems, Melbourne School of Engineering, at the University of Melbourne (Australia). He has a strong background in Theoretical Computer Science, Software Engineering, and Business Process Management from the National University of Kyiv-Mohyla Academy (Ukraine), Hasso Plattner Institute (Germany), and the University of Potsdam (Germany). In March 2012, he received a Ph.D. degree (Dr. rer. nat.) in the scientific discipline of Computer Science from the University of Potsdam (Germany). His research and teaching interests include Computing Systems, Information Systems, Distributed Systems, Process Modeling and Analysis, Data Science, Business Process Management, and Algorithms. Artem Polyvyanyy has published over 70 papers on these topics, including academic book chapters, peer-reviewed journal articles, and refereed papers at international conferences and workshops. He has actively contributed to several open-source initiatives that led to a significant impact on research and practice, including jBPT, Oryx, and Apromore. His research focuses on Process Mining and Process Querying. The research discipline of Process Mining combines studies of inferences from data in Data Mining and Machine Learning with Process Modeling and Analysis to tackle the problems of discovering, monitoring, and improving real-world processes, while Process Querying combines concepts from Big Data and Process Modeling and Analysis with Business Process Intelligence and Process Analytics to study techniques for retrieving and manipulating models of processes, both real-world and envisioned, to systematically organize and extract process-related information for subsequent use.

Dr. Jan Martijn van der Werf is an assistant professor at Utrecht University on architecture mining, combining process mining with software architecture. His research and teaching focuses on modeling, analyzing and reconstructing interactions between components in large, complex software systems. He is interested in how formal methods, such as Petri nets, can be used in practice to study the dynamics of large systems. Jan Martijn van der Werf is active in both the process mining community, as well as in the software architecture community, where he publishes regularly at international conferences and workshops. He holds a joint PhD degree from Eindhoven University of Technology and the Humboldt Universität zu Berlin on the compositional design and verification of component-based information systems.

Title: Driving Digitalization on the Shopfloor Through Flexible Process Technology

Abstract: The current crisis shows that digitalization has become more crucial than ever. We believe that process technology constitutes THE vehicle to drive digital transformation throughout all application domains. In this tutorial, we reflect on the opportunities of process technology in more "physical" environments such as industrial manufacturing with machines, sensors, and manual work. For this, the tutorial discusses and combines questions in the areas of flexible process technology, Internet of Things, and industrial manufacturing processes. The tutorial is outlined as follows: a) introduction into flexible process technology; b) introduction to a real-world industrial manufacturing case; c) solution based on the secure manufacturing orchestration platform centurio.work which is already applied in several real-world industrial settings; d) benefits of a process-oriented solution such as vertical and horizontal integration as well as contextualized data collection and integration of the activities of the employees.

Author:

  • Stefanie Rinderle-Ma (University of Vienna, Austria)

Author Bio:

Stefanie Rinderle-Ma is a full professor at the Faculty of Computer Science, University of Vienna (Austria) where she heads the Research Group Workflow Systems and Technology. Stefanie received her PhD and habilitation degree in Computer Science from Ulm University, Germany. Her research focuses on distributed and flexible process technology, process and data science, as well as digitalized compliance management. Application areas comprise manufacturing and health care.

Title: Business Process Analysis using Scripting Languages.

Abstract: During the recent decade, various (commercial) software solutions have been developed that support the semi-automated analysis of business processes, i.e., known as process mining solutions. Examples include, and are not limited to, Celonis, Disco, ProcessGold, myInvenio, on the commercial side, and, ProM, Apromore, RapidProM, on the open-source/academic side. More recently, several process mining techniques have been developed in the context of scripting languages, e.g., Python, R, etc. The advantage of using scripting languages, which are often interpreted, w.r.t. compiled programming languages, include flexibility, rapid prototyping, portability, etc. In this tutorial, we focus on two, recently developed software libraries, i.e., pm4py and bupaR, developed for python and R respectively. We sketch the main functions of the two libraries, and compare their strengths and weaknesses. For both libraries, importing event data will be discussed. In the context of pm4py, we furthermore focus on applying process discovery and conformance checking. In the context of bupaR, we focus more on visualization of event data for descriptive and exploratory analysis, as well as declarative conformance checking. This tutorial is intended for academics, data scientists, software scientists and process (intelligence) consultants, and might additionally be interesting for process owners and department heads/managers. We also aim to discuss the applicability and limitations of scripting languages for the development of novel (enterprise-grade) process mining technologies.

Authors:

  • Gert Janssenswillen (Hasselt University, Belgium)
  • Sebastiaan J. van Zelst (Fraunhofer Institute for Applied Information Technology, RWTH Aachen University, Germany)

Authors Bio:

Gert Janssenswillen obtained his PhD on the topic of conformance checking and reproducible process mining at Hasselt University. Here, he is currently a member of the Business Informatics Applied Research Unit. His research interests include: conformance checking, data-driven customer journey analysis, stochastic process discovery, learning analytics and storytelling with data.

Sebastiaan J. van Zelst received his PhD degree in the area of online process mining from Eindhoven University of Technology. Currently, he is working as a scientist at the Fraunhofer Institute for Applied Information Technology (FIT), and he is a member of the Process and Data Science Chair of the RWTHAachen University. His research interests include: data-driven process optimization, process monitoring and prediction, event abstraction in process mining and interactive process discovery.

Title: Predictive Process Monitoring: From Theory to Practice

Abstract: Predictive Process Monitoring is a branch of process mining that aims at predicting, at runtime, the future development of ongoing cases of a process. Predictions related to the future of an ongoing process execution can pertain to numeric measures of interest (e.g., the completion time), to categorical outcomes (e.g., whether a given predicate will be fulfilled or violated), or to the sequence of future activities (and related payloads). Recently, different approaches have been proposed in the literature in order to provide predictions on the outcome, the remaining time, the required resources as well as the remaining activities of an ongoing execution, by leveraging information related to the control flow, the data flow, or even unstructured text contained in event logs recording information about process executions. The approaches can be of different nature and, some of them also equipped to offer users support in tasks such as parameter selection. This tutorial aims at (i) providing an introduction on predictive process monitoring, including an overview on how to move within the large number of approaches and techniques available; (ii) introducing the current research challenges and advanced topics; (iii) providing an overview on how to use the existing instruments and tools.

Authors:

  • Chiara Di Francescomarino (Fondazione Bruno Kessler (FBK), Italy)
  • Chiara Ghidini (Fondazione Bruno Kessler (FBK), Italy)
  • Fabrizio Maria Maggi (Free University of Bozen-Bolzano, Italy)
  • Williams Rizzi (Free University of Bozen-Bolzano, Italy)

Authors Bio:

Chiara Di Francescomarino is a researcher at Fondazione Bruno Kessler (FBK) in the Process and Data Intelligence (PDI) Unit. She received her PhD in Information and Communication Technologies from the University of Trento, working on business process modeling and reverse engineering from execution logs. She is currently working in the field of process mining, investigating problems related to process monitoring, process discovery, as well as predictive process monitoring based on historical execution traces. She has published papers in the top business process conferences and journals (e.g., BPM, TKDE and IS) and she has worked in local and international research projects. She serves as PC member in top conferences in the business process management field and as peer reviewer in international journals.

Chiara Ghidini is a senior Research Scientist at Fondazione Bruno Kessler (FBK), Trento, Italy, where she heads the Process & Data Intelligence (PDI) research unit. Before joining FBK in 2003 she was a post-doc at the Centre for Agent Research and Development, Manchester Metropolitan University (1998-2000), and a lecturer at the Department of Computer Science, University of Liverpool (2000-2003). She obtained her PhD in Computer Science Engineering in a joint programme between the Università “La Sapienza” of Rome and the University of Trento in 1998. Her scientific work in the areas of Semantic Web, Knowledge Engineering and Representation, Multi-Agent Systems and Process Mining is internationally well known and recognised, and she has published more than 100 papers in those areas. Dr. Ghidini has acted as PC or track chair in the organisation of workshops and conferences on multiagent systems (EUMAS'04), Contexts-based representations (Context-03), Knowledge Engineering and Capturing (EKAW 2018), Semantic Web (ESWC 2012 and ISWC 2019) and Business Process Management (BPM2020). In addition, she has served as programme committee member for most of the top international conferences in these areas. She has been involved in a number of international research projects, among which the FP7 Organic.Lingua and SO-PC-Pro European projects, as well as industrial projects in collaboration with companies in the Trentino area.

Fabrizio Maria Maggi received his PhD degree in Computer Science in 2010. He was postdoc at the Architecture of Information Systems (AIS) research group - Department of Mathematics and Computer Science - Eindhoven University of Technology from 2010 to 2012 and he was part of the Software Engineering & Information Systems Group - Institute of Computer Science - University of Tartu, first as Research Fellow (2013-2017) and then as Associate Professor (2017-2020). He is currently Associate Professor at the Research Centre for Knowledge and Data (KRDB) - Faculty of Computer Science - Free University of Bozen-Bolzano. His research interest has focused in the last years on the application of Artificial Intelligence to Business Process Management. He authored more than 120 articles on process mining, declarative and hybrid business process notations, business constraint verification and monitoring, predictive business process monitoring, DMN decision tables for business process decision modeling. He serves as program committee member of the top conferences in the field of Business Process Management and Information Systems. He is committee member of the journal track of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD).

Williams Rizzi received his bachelor’s and master’s degree in Computer Science from the University of Trento, Italy. He is currently a PhD student in Computer Science enrolled in a joint PhD Programme between Fondazione Bruno Kessler and Free University of Bozen-Bolzano, Italy. His research interests focus on the application of Machine Learning techniques to the Predictive Process Monitoring domain and he is currently actively developing Nirdizati, one of the state-of-the-art Predictive Process Monitoring tools. He took part in both industrial and research projects at the University of Trento and Fondazione Bruno Kessler. He has been publishing several papers on Predictive Process Monitoring (including one recognized with a distinguished paper award) in the top conferences in the field of Business Process Management and Information Systems since 2015.

Title: Queue Mining: Process Mining Meets Queueing Theory

Abstract: The tutorial will expose the audience to queue mining, which is a set of novel data-driven techniques used for modeling and analyzing complex resource-driven environments. Queue mining was born from the synergy between process mining and queueing theory. From automated discovery, through conformance checking, to predictive monitoring, process mining plays a key role in modern process-oriented data analysis. Historically, process mining has mainly focused on the single-case perspective, while in reality, performance of processes is highly influenced from correlations between running cases. Queueing theory, in turn, is a well-established paradigm in Operations Research that addresses this gap. It revolves around processes that exhibit scarce resources and highly correlated cases that compete for these resources. In the first part of the tutorial, we shall present a high-level overview of queue mining methodologies. Specifically, we will discuss a range of queue mining methods that involve predictive monitoring in various queueing settings, conformance checking in queue-driven systems, and a generalized congestion-driven approach for predicting remaining times and analyzing bottlenecks. Subsequently, we shall demonstrate the usefulness of queue mining in real-life applications coming from three service domains: call centers, public transportation, and healthcare. We will conclude the tutorial with a discussion of novel research directions that involve queue mining and its extensions into other evolving fields. We believe that the tutorial will attract both researchers and practitioners in the area of process management and mining, who are interested in performance analysis, predictive monitoring, and operations management.

Authors:
- Avigdor Gal (Technion – Israel Institute of Technology, Israel) - Arik Senderovich (University of Toronto, Canada) - Matthias Weidlich (Humboldt-Universit¨at zu Berlin, Germany)

Authors Bio:

Avigdor Gal is a faculty member at the Technion - Israel Institute of Technology, were he leads the Data Science & Engineering program. He specializes in various aspects of data management and mining with about 150 publications in journals (Journal of the ACM (JACM), ACM Transactions on Database Systems (TODS), IEEE Transactions on Knowledge and Data Engineering (TKDE), Information Systems, and the VLDB Journal), books, and conferences (ICPM' BPM, CAiSE, SIGMOD, VLDB, ICDE, CIKM, ER, CoopIS). He served as a program co-chair and general co-chair of several conferences, including BPM and DEBS. In the past he gave tutorials in SIGMOD, VLDB, ICDE, EDBT, and CAiSE. Avigdor Gal is a recipient of the prestigious Yannai award for excellence in academic education.

Arik Senderovich is an Assistant Professor of Human-Centered Data Science at the Faculty of Information at University of Toronto. Before his appointment at the Faculty of Information, he received the Lyon Sachs scholarship (awarded to one PhD grad per year) and worked as a postdoctoral fellow in the Toronto Intelligent Decision Engineering Laboratory (TIDEL) at the University of Toronto. He received his Ph.D. in the area of process mining, focusing on queueing perspectives in process mining, from the Technion - Israel Institute of Technology in 2016, for which he also received the 2017 best dissertation award at BPM, the annual Business Process Management conference. Arik's research focuses on data analytics in business processes, with emphasis on complex systems with scarce resources. He published papers on the above in journals, and leading conferences in the field.

Matthias Weidlich is a full professor at the Department of Computer Science at Humboldt-Universität zu Berlin (HU). Matthias' research focuses on process-oriented and event-based information systems. His results appear regularly in premier conferences (SIGMOD, VLDB, ICDE, IJCAI, BPM, CAiSE) and journals (TSE, TKDE, Information Systems, VLDB Journal) in the field. He is a Junior-Fellow of the German Informatics Society (GI) and in 2016 received the Berlin Research Award (Young Scientist). He acted as PC Co-Chair of the BPM 2015 conference, served as PC Co-Chair of the ACM DEBS 2018 conference, and is an area editor for Elsevier's Information Systems. In the past, he gave tutorials at ICDE, CAiSE, AAMAS, and DEBS.