The 2nd International Workshop on the Induction of Process Models
(IPM?08) at ECML PKDD 2008, 15 September 2008, Antwerp, Belgium
http://wwwkramer.in.tum.de/ipm08/


* Call for Abstracts (deadline June 16th)

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 by email to ipm08@in.tum.de . 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?t M?nchen, Germany
Mykola Pechenizkiy, Eindhoven University of Technology, The Netherlands
Ljupco Todorovski, University of Ljubljana, Slovenia