The 2nd International Workshop on the Induction of Process Models (IPM'08)
at ECML PKDD 2008, 15 September 2008, Antwerp, Belgium Call for Papers While the worlds of science and business typically meet in the presence of a profitable scheme, individuals from both environments have interests in analyzing complex data about dynamic systems. Whether motivated by a drive to increase system efficiency or to understand nature, their shared goal leads to a shared focus on the underlying causal processes that explain or produce observed phenomena. To this end, researchers construct models from data derived from observed system behavior and background knowledge about the candidate processes. Traditional literature on regression, time-series analysis, and data mining produces descriptive models that may reproduce the observed data but cannot explain the principal dynamics. Therefore, researchers are called to develop methods that capture complex temporal and spatial relationships in terms of domain knowledge (e.g., relevant scientific or business concepts) and that construct these explanatory process models. One can develop both qualitative and quantitative process models depending on their intended use. Qualitative approaches to model induction include learning state transition models, Petri-nets, and learning from (time-stamped) event sequences and event logs. Qualitative representations are particularly interesting for business applications that aim to discover business processes from data. Examples of event logs include process data generated by administrative services, health care data about patient handling, and logs of workflow tools. In comparison, quantitative approaches to model construction are grounded in standard mathematical representations (e.g., systems of differential equations). Quantitative representations are common in scientific applications, and are especially prominent in the environmental and biological sciences that deal with complex, natural systems. Notably, the business and scientific worlds are not separated by an interest in the qualitative or quantitative emphasis of their models. Moreover, researchers working in these domains would benefit from approaches that integrate the qualitative and quantitative aspects of system behavior. In this workshop, we aim to attract researchers with an interest in inductive process modeling in different formalisms including Petri nets, qualitative and quantitative processes, differential equations, episode rules, logical rules, and others. Also, although we have emphasized the business and scientific domains, we are open to any application of process model induction. A non-exhaustive list of topics includes: * learning structured process models such as Petri net or process algebra models from event logs * modeling techniques for describing the structure of event data such as Markov models * learning differential equation models * learning in qualitative reasoning representations * learning in temporal logic * learning logical models of state transitions (e.g., by recursive clauses) * learning from time-stamped event sequences (e.g., episode rules) * learning from large databases of trajectories * connectionist/subsymbolic models of sequence learning * scalable and robust process mining algorithms and techniques * process mining evaluation: metrics, approaches and frameworks * the adaption of web mining, text mining, temporal data mining approaches for inductive process modeling * particularly welcome are case studies and applications (e.g., from business, the environmental, medical or biological sciences) and discussions of the lessons learned from such case studies * and papers identifying open problems such as dealing with missing and/or noisy data, regularization, incorporating background/domain knowledge, efficient search through the space of candidate process-based models, ... Inductive process modeling and process mining are challenging research areas that have the potential to grow in importance like graph or sequence mining. On the other hand, process mining can benefit from the input of related fields in data mining and machine learning, such as temporal data mining, episodes and web log mining. In the ECML/PKDD 2008 workshop on the induction of process models, we intend to bring scientists together and actively identify common research threads, define open problems, and develop collaborative contacts. It should provide a more relaxed atmosphere than a conference setting where participants are encouraged to ask clarifying questions throughout the talks and to move past jargon-induced barriers. Submission Extended abstracts (two pages in Springer format) should be submitted by June 16th, 2008. Final versions of accepted papers will appear in the informal ECML/PKDD workshop proceedings and will be made available on the workshop website before the workshop takes place. Submission implies the willingness of at least one of the authors to register and present the paper. Authors of accepted abstracts will be asked to submit a short 4 to 8 page paper in PDF format (following the Springer LNCS guidelines for preparing manuscripts) that describes their research in more detail. Important Dates * Abstracts due June 16th * Author Notification on June 30th * Final Papers due August 4th * Workshop September 15th Organizing Committee * Will Bridewell, Stanford University, USA * Toon Calders, Eindhoven University of Technology, The Netherlands * Ana Karla de Medeiros, Eindhoven University of Technology, The Netherlands * Stefan Kramer, Technische Universität München, Germany * Mykola Pechenizkiy, Eindhoven University of Technology, The Netherlands * Ljupco Todorovski, University of Ljubljana, Slovenia
---- [[ Petri Nets World: ]] [[ http://www.informatik.uni-hamburg.de/TGI/PetriNets/ ]] [[ Mailing list FAQ: ]] [[ http://www.informatik.uni-hamburg.de/TGI/PetriNets/pnml/faq.html ]] [[ Post messages/summary of replies: ]] [[ [email protected] ]]
