Special Session on Process Mining for the 2015 IEEE Symposium on Computational Intelligence and Data Mining

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1 Special Session on Process Mining for the 2015 IEEE Symposium on Computational Intelligence and Data Mining Organizers Name: Andrea Burattin Affiliation: University of Innsbruck, Austria One paragraph short bio: Andrea Burattin is working as a post-doc at the University of Innsbruck, Austria. Previously, he has been working as a postdoc at the University of Padua, Italy. In 2013, he received the PhD degree in Computer Science from the University of Bologna and the University of Padua. The IEEE Task Force on Process Mining awarded the Best Process Mining Dissertation Award for to his PhD thesis. The thesis has then been published as a Springer monograph entitled Process Mining Techniques in Business Environments. During his PhD, he spent several months at the Technical University of Eindhoven. He organized special sessions on process mining at CIDM 2013 and Name: Fabrizio M. Maggi Affiliation: University of Tartu, Estonia f.m.maggi@ut.ee One paragraph short bio: Fabrizio Maria Maggi received his PhD degree in Computer Science in 2010 from the University of Bari, Italy, and after a period at the Architecture of Information Systems (AIS) research group - Department of Mathematics and Computer Science - Eindhoven University of Technology, he is currently a research fellow at the Software Engineering Group - Institute of Computer Science - University of Tartu. He authored more than 40 articles in journals, such as ACM Transactions on Intelligent Systems and Technology, Information Systems, and highly competitive conference including BPM, CAiSE, Petri nets, and CoopIS. The main topics of his publications are process mining, (declarative) business process modeling and business constraints/rules, monitoring of business constraints at runtime. Name: Chiara Di Francescomarino Affiliation: Fondazione Bruno Kessler, Italy 1

2 One paragraph short bio: Chiara Di Francescomarino is a researcher at Fondazione Bruno Kessler (FBK) in the Shape and Evolve Living Knowledge (SHELL) Unit. She received her PhD in Information and Communication Technologies at FBK and University of Trento, Italy, in During the PhD she has investigated topics related to business process modeling and reverse engineering of processes from Web Applications. She has later on worked on the collaborative modelling and the empirical evaluation of tools and techniques for its support and she is currently working in the field of business processes, by extending her research interest from business process modeling to process execution (process monitoring, repair as well as predictions and recommendations). Introduction to the Special Session The goal of this special session is to allow experts in the area of process mining and (big) data analysis to share new techniques, applications and case studies. This session is organized by the IEEE Task Force on Process Mining. We now live in a time where the amount of data created daily goes easily beyond the storage and processing capabilities of nowadays systems: organizations, governments but also individuals generate large amounts of data at a rate that has started to overwhelm the ability to timely extract useful knowledge from it. Nevertheless the strategic importance of the knowledge hidden in such data, for effective decision making is paramount and need to be extracted quickly in order to effectively react to dynamic situations. Efficient stream processing approaches for real time analysis are crucial for enabling the predictive capabilities required by today s dynamically and rapidly evolving enterprises. Moreover, since the work of medium-large enterprises is typically governed by business processes, it is very common to have event data generated as result of such process executions. Process mining is a relatively young research discipline that sits between computational intelligence and data mining on the one hand and process modeling and analysis on the other hand. The idea of process mining is to discover, monitor and improve real processes (i.e., not assumed processes) by extracting knowledge from event logs readily available in today s systems. Process mining provides an important bridge between data mining and business process analysis. Under the Business Intelligence (BI) umbrella many buzzwords have been introduced to refer to rather simple reporting and dashboard tools, such as BAM, CEP, CPM, CPI, BPI, TQM and Six Sigma. These approaches have in common that processes are put under a microscope to see whether further improvements are possible. Process mining is an enabling technology for CPM, BPI, TQM, Six Sigma, and the like. 2

3 Over the last decade, event data have become readily available and process mining techniques have matured. Process mining algorithms have been implemented in various academic and commercial systems. Today, there is an active group of researchers working on process mining and it has become one of the hot topics in Business Process Management (BPM) research. Moreover, there is a huge interest from industry in process mining. More and more software vendors are adding process mining functionality to their tools. Moreover the level of maturity and the relative low-cost of distributed approaches for storage and processing of information has not been fully exploited by the process mining community. There are very few research results on distributed storage methods and process mining algorithms. Considering all these aspects, a special session on process mining can improve the value of the conference by enhancing awareness of typical problems and issues of process mining. Moreover, it is possible to get inspired from classical data mining approaches and methodologies in order to improve analysis of data coming from information systems. Possible topics of interest: Storage and extraction of big process logs Process mining approaches Online process mining (stream processing) Distributed approaches for process mining Event log obfuscation Privacy-aware process mining Business process intelligence Data mining for process management Specific computational intelligence applications in process mining Case studies and empirical evaluations Previous editions of this special session have been organized for CIDM (2011, 2013, and 2014) and WCCI (2010 and 2012). About the IEEE Task Force on Process Mining The task force was established in 2009 in the context of the Data Mining Technical Committee (DMTC) of the Computational Intelligence Society (CIS) of the Institute of Electrical and Electronic Engineers (IEEE). The current task force has members representing software vendors (e.g., Perceptive Software, Software AG, HP, IBM, Infosys, Fluxicon, Businesscape, Iontas/Verint, Fujitsu, 3

4 Fujitsu Laboratories, Business Process Mining), consultancy firms/end users (e.g., ProcessGold, Business Process Trends, Gartner, Deloitte, Process Sphere, Siav SpA, BPM Chili, BWI Systeme GmbH, Excellentia BPM, Rabobank), and research institutes (e.g., TU/e, University of Padua, Universitat Politcnica de Catalunya, New Mexico State University, Technical University of Lisbon, University of Calabria, Penn State University, University of Bari, Humboldt- Universitt zu Berlin, Queensland University of Technology, Vienna University of Economics and Business, Stevens Institute of Technology, University of Haifa, University of Bologna, Ulsan National Institute of Science and Technology, Cranfield University, K.U. Leuven, Tsinghua University, University of Innsbruck, University of Tartu). Concrete objectives of the task force are: to make end-users, developers, consultants, business managers, and researchers aware of the state-of-the-art in process mining; to promote the use of process mining techniques and tools and stimulate new applications; to play a role in standardization efforts for logging event data; to organize tutorials, special sessions, workshops, panels, and; to publish articles, books, videos, and special issues of journals. Since its establishment in 2009 there have been various activities related to the above objectives. For example, several workshops and special tracks were (co-)organized by the task force, e.g., the workshops on Business Process Intelligence (BPI ) and special tracks at main IEEE conferences (e.g., CIDM 2011, CIDM 2013, CIDM 2014 and WCCI 2012). The task force organized a special issue of IEEE Transactions on Services Computing on Processes meet Big Data 1. Knowledge was disseminated via tutorials (e.g., WCCI 2010 and PMPM 2009), summer schools (ESSCaSS 2009, ACPN 2010, CICH 2010, etc.), videos 2, and several publications including the first book on process mining recently published by Springer. The task force also (co- )organized the Business Process Intelligence Challenges (BPIC ), a competition where participants had to extract meaningful knowledge from a large and complex event log. In 2010, the task force also defined XES 3, a standard logging format that is extensible and supported by the OpenXES library 4 and by tools such as ProM, XESame, Disco, Nitro. During the 2014 AdCom meeting (July in Beijing, China), the IEEE CIS AdCom approved the motion to sponsor the XES standard. In the process of gaining this approval, a webinar on the XES standard has been created. 5 The reader is invited to visit 1 processes_meet_big_data

5 for more information about the activities of the task force. Process Mining Manifesto In the context of the IEEE Task Force on Process Mining, a group of more than 75 people created the Process Mining Manifesto. By defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the visibility of process mining as a new tool to improve the (re)design, control, and support of operational business processes. Currently the manifesto is available in 13 languages: Chinese, Dutch, English, French, German, Greek, Italian, Japanese, Korean, Polish, Portuguese, Spanish and Turkish. Links: IEEE Task Force on Process Mining: Process Mining Manifesto 6, DOI: / Potential Contributors Marta Cimitile, Unitelma Sapienza University, Rome, Italy. Mario Luca Bernardi, University of Sannio, Benevento, Italy. Claudio Di Ciccio, Vienna University of Economics and Business, Austria. Amin Jalali, Stockholm University, Sweden. Tijs Slaats, IT University of Copenhagen, Denmark. Cristina Cabanillas, Vienna University of Economics and Business, Austria. Marco Comuzzi, City University London, UK