Business Processing Intelligence (BPI)
New information:
Program (see workshop program)
Abstract of the keynote speaker (see
keynote abstract)
Look here for the Springer BPM'05 workshop
proceedings.
Some workshop organizers including us, have agreed to have a common deadline for a subset of workshops at BPM 2005. Consequently the paper deadline for BPI'05 has been moved to April 1st.
Submission of Papers: April 1, 2005 (old
March 18)
Notification of acceptance: May 25, 2005 (old May 11)
Camera ready paper: June 20, 2005 (old May 27)
Conference days: September 6-7, 2005
Workshops day: September 5, 2005
Business Process Intelligence (BPI) is an emerging
area that has been taking increasing importance during the last years as a
consequence of the pressing need
that companies are experiencing to improve the business processes underlying
their business operations to better meet their business goals. A number of
groups in different research areas are working on technologies to support
different aspects of BPI, even if they do not call it that way. Many other names
exist for such technologies, and there is a confusion among the exact meaning of
names like BAM (Business Activity Monitoring), BOM (Business Operations
Management), BPM (Business Performance Management), among others. The reality is
that there is much overlap among techniques and tools supporting all these
technologies. The realization of a workshop on BPI gives the opportunity to
consolidate this field and start building a community that
recognizes BPI as an area encompassing all these technologies with the
end goal of improving business operations.
- Process discovery: this refers to the analysis of enterprise operations in order to derive the process models that these operations obey. It may be useful for users to better understand their operations and it can be the first step that leads to supporting the process with a workflow tool. It can also be used to reengineer an existing process model to make it more efficient.
- 'Intelligent' process analysis: this refers to the analysis of business process execution to discover interesting correlations, e.g., between process data and resources and business metrics, to perform capacity planning, or to identify the causes of low-quality process executions (whatever quality may mean for the analyst). For example, users may be interested in discovering under which situations a certain exception is raised, or the process follows a certain path, or leads to a certain outcome.
- Prediction: besides analyzing the value of business metrics and understanding, among other things, the causes of low-quality process executions, BPI aims at predicting critical situations (e.g., an exception, or a delay) on a running process instance before it actually happens. Ideally, predictions are made at the early stages of execution a process instance, and are then refined as the execution progresses and more data becomes available.
- Exception handling: once a problem has been recognized (or predicted), another goal of BPI is to assist the analyst in making decisions to address the problem. This may be for example based on mining how similar problems were
successfully handled in the past.
- Static optimization: the intelligent analysis described above may lead to the identification of areas of optimization for a process, for example in terms of different sizing of resource pools, different resource assignment criteria, and the like. BPI offers support for optimizing the process configuration to improve upon those areas .
- Dynamic optimization: ideally, one could think of an intelligent component that constantly manages and supervises each process instance (in a controlled way), for example by having influence in routing and task assignment decisions in order to maximize certain business objectives.
Research papers covering these and other sub-areas are highly sought for this workshop. In addition, since BPI is an area that is still immature and controversial, position papers are welcome. In particular, there are different opinions about whether BPI is just the application of known business intelligence and data mining techniques to the business process domain or there is something fundamentally new that requires new developments and research on new techniques. For example, are standard feature selection techniques in data mining enough or do we need more powerful techniques that select/create features that are more meaningful to high level business managers/executives? Are current techniques capable of identifying at which moments during the execution of process instances it is most convenient to update predictions about their metric outcomes so that it is not necessary to create prediction models and update predictions for each possible execution stage? Do we need new abstractions that make possible to structure the analysis in a way that responds better to the requirements of business executives? Other aspects of BPI like ultimate goals, level of automation, real-time readiness, business impact, and others are good candidates for experience papers .
Intended audience and prerequisites for participants
Intended audience
This workshop intends to bring together researchers and practitioners to discuss the key research issues, approaches, innovative applications, enabling technologies and trends in this area. The program committee seeks position papers, high quality technical papers, and challenging experience papers.
Prerequisites for participants
For most attendees the main prerequisite will be to have an accepted paper. This prerequisite aims to foster interaction and exchange of ideas among experienced participants to create an enriching environment for everybody. However, other high qualified participants from industry and academia will also be invited to participate. In addition a few spaces will be given to PhD students that are interested in this area to give them the opportunity to get more involved.
Expected number of participants
The ideal number of participants is between 15 and 20.
Proceedings
On-site
proceedings and/or post-proceedings printed by Springer (the conference
organization committee is in negotiation with Springer).
The
result of the negotiation appears positive!. Details will be given in the
Notification of acceptance of submitted papers.
Topics
Areas of particular interest for the workshop include (but are not limited to):
Papers about work in progress and comparative studies are specially welcome.
Selection processProspective authors are invited to submit papers for presentation in any of the areas listed above. Only papers in English will be accepted, and the length of the paper should not exceed 10 pages (was 12 pages but Springer asked for 10 pages). Papers should be formatted in LNCS format (see http://www.springer.de/comp/lncs/authors.html for details). The title page must contain a short abstract, a classification of the topics covered, preferably using the list of topics above, and an indication of the submission category (regular paper/position paper/industry paper). Papers should be submitted electronically via BPI workshop management system at side: http://ga2379.tm.tue.nl/BPI05/. Please submit a self-contained PostScript file or PDF file. For submitting your paper you can click on the "Authors" link on the top bar of this page and read the detailed instructions. You will be asked to submit your abstract and contact data first, and then you will receive by email the username and password for logging in and submitting the paper.
Format of the workshopProgram Chairs
- Malu Castellanos, HP Labs, USA
- Ton Weijters, University of Eidhoven, The Netherlands
Program members
- Wil Van der Aalst, University of Eindhoven
- Boualem Benatallah, University of New South Wales, Australia
- Fabio Casati, Hewlett-Packard, USA
- Jonhatan E. Cook, New Mexico State University, USA
- Umesh Dayal, HP Labs, USA
- Peter Dadam, University of Ulm, Germany
- Marlon Dumas, Queensland University of Technology, Australia
- Gianluigi Greco, University of Calabria
- Dimitrios Georgakopoulos, Telcordia Technologies, Austin, USA
- Mati Golani, Technion, Israel
- Joachim Herbst, DaimlerChrysler Research and Technology, Germany
- Ramesh Jain, Georgia Tech, USA
- Cesare Pautasso, ETH Zurich, Switserland
- Shlomit S. Pinter, IBM Haifa Research Lab, Israel
- Michael Rosemann, Queensland University of Technology, Australia
- Marek Rusinkiewicz, Telcordia Research, USA
- Pnina Soffer, Haifa University, Israel
- Hans Weigand, Infolab, Tilburg University
- Mathias Weske, Hasso Plattner Institute at University of Potsdam
- Michael zur Muhlen, Stevens Institute of Technology, USA
Publication and coordination chair
Manolo Carcia-Solaco, CMU-West, USA (email: manolo@andrew.cmu.edu )
Organizers and affiliations
|
Malu Castellanos (main contact)
|
Ton Weijters
|
Malu Castellanos is a senior researcher in the Intelligent Enterprise Technologies Laboratory at Hewlett-Packard where she has been working since 1997. Her main current project is the Business Cockpit which is a BPI platform being developed at HP Labs. She received a B.S. in Computer Engineering at the National University of Mexico, Mexico City and a Ph.D. in Computer Science from the Polytechnic University of Catalunya, Barcelona where she was a professor prior to joining Hewlett-Packard. She has more than 30 publications in international conferences, journals and book chapters and has actively participated in the organization and PC of various international conferences and workshops.
Ton Weijters is associate professor at the technology management department of the Eindhoven University of Technology (TUE), and member of the BETA research group. Currently working on (i) the application of Knowledge Engineering and Machine Learning techniques for process mining, planning and scheduling (ii) fundamental research in the domain of Machine Learning and Knowledge Discovering. He is the auteur of many scientific publications in the mentioned research fields.