Twelfth International Workshop on Business Process Intelligence (BPI’16) to be held in conjunction with BPM’16

The BPI 2016 workshop will be held on 19 September 2016. The accepted papers will be presented during the workshop and authors have 25 minutes for their presentations and 15 minutes for discussion.

At the end of the workshop, the winner for the sixth international Business Process Intelligence Challenge (BPI Challenge) and of the first Process Mining Contest will be announced.

Business Process Intelligence is an area that spans process mining (i.e., process discovery, conformance checking, etc.), predictive analytics and many other techniques that are all gaining interest and importance in industry and research. BPI refers to the application of various measurement and analysis techniques in the area of business process management. In practice, BPI is embodied in tools for managing process execution quality by offering several features such as analysis, prediction, monitoring, control, and optimization.

The goal of this workshop is to provide a better understanding and a more appropriate support of company processes at design time and the way they are handled at runtime, focussing on processes in isolation as well as the interplay between many parallel processes both within and between companies.

We aim to bring together practitioners and researchers from different communities such as business process management, information systems research, business administration, software engineering, artificial intelligence, process mining, and data mining who share an interest in the analysis of business processes and process-aware information systems. The workshop aims at discussing the current state of ongoing research and sharing practical experiences.

The list of topics that are relevant to the BPI workshop includes the following, but is not limited to:

Analysis Techniques at design time and/or runtime:

• Mining of business processes from event logs
• Mining of non process aware systems / event streams
• Multi perspective process mining
• Statistical analysis in the business process management lifecycle
• Predictive analytics
• Recommender systems
• Conformance / compliance analysis
• Root cause analysis for process deviations
• Business process visualization
• Visualization of process mining results
• Machine-learning and business processes
• Measurement of business process models and business process modeling
• Information retrieval related to business process management
• Similarity related to processes and cases
• Integration of processes and process models
• Mathematical optimization of business processes
• Simulation of business processes

Applications of such analysis techniques and case studies in:

• Performance Measurement of business processes
• Business Process Reengineering
• Business Process Quality
• Emergent workflows
• Process discovery
• Conformance and risk management for business processes
• Operations Management and Six Sigma
• Data warehousing
• Static and dynamic optimization
• Self-management
• Monitoring of business processes
• Resource Allocation in business processes
• Prediction
• Dynamic composition of business processes

Submitted papers will be evaluated on the basis of significance, originality and technical quality. The length of a paper must not exceed 12 pages, and there is no possibility to buy additional pages. Authors are requested to prepare submissions according to the LNCS/LNBIP format specified by Springer. The title page must contain a short abstract and a classification of the topics covered, preferably using the list of topics above. Papers should be submitted electronically through easychair. Members of an international and solid program committee will review all submissions. Each paper will be reviewed by 3 PC members guaranteeing that only papers presenting high quality and innovative research and practice issues in areas relevant to the workshop theme will be accepted.

The workshop is co-organized by the IEEE Task Force on Process Mining. The goal of this Task Force is to promote the research, development, education and understanding of process mining. For more information about the activities of the IEEE Task Force on Process Mining and its members see https://www.win.tue.nl/ieeetfpm/

• Artur Caetano, Universidade de Lisboa, Portugal
• Ana Karla Alves de Medeiros, Centraal Beheer Achmea, The Netherlands
• Diogo R. Ferreira, IST, University of Lisbon, Portugal
• Walid Gaaloul, Insitut Telecom, France
• Gianluigi Greco, University of Calabria, Italy
• Daniela Grigori, Paris-Dauphine University, France
• Antonella Guzzo, University of Calabria, Italy
• Anna Kalenkova, Higher School of Economics, Russia
• Michael Leyer, Frankfurt School of Finance & Management, Germany
• Jan Mendling, Wirtschaftsuniversität Wien, Austria
• Jorge Munoz-Gama, Pontificia Universidad Católica de Chile, Chili
• Viara Popova, Tallinn University of Technology, Estonia
• Manfred Reichert, University of Twente, The Netherlands
• Andreas Rogge-Solti, Wirtschaftsuniversität Wien                         
• Michael Rosemann, Queensland University of Technology, Australia
• Anne Rozinat, Fluxicon, The Netherlands
• Domenico Sacca, Università degli studi della Calabri, Italy
• Pnina Soffer, University of Haita, Israel
• Alessandro Sperduti, Padua University, Italy
• Suriadi Suriadi, Massey University, New Zealand
• Seppe vanden Broucke, KU Leuven, Belgium
• Wil van der Aalst, Eindhoven University of Technology, The Netherlands
• Eric Verbeek, Eindhoven University of Technology, The Netherlands
• Hans Weigand, University of Tilburg, The Netherlands

• BPI 2015
• BPI 2014
• BPI 2013
• BPI 2012
• BPI 2011
• BPI 2010
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• BPI 2007
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• BPI 2005