From: "Alves De Medeiros, A.K." <A.K.Medeiros@tue.nl>
To: petrinet@informatik.uni-hamburg.de
Date: Thu, 29 May 2008 14:53:32 +0200
Subject: (PN) (Call for papers) The 2nd International Workshop on the
Induction of Process Models (IPM'08) at ECML PKDD 2008,
15 September 2008, Antwerp, Belgium
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 Universit=E4t M=FCnchen, Germany
* Mykola Pechenizkiy, Eindhoven University of Technology, The
Netherlands
* Ljupco Todorovski, University of Ljubljana, Slovenia
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