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KDPM 2009 : Knowledge Discovery Meets Process Mining Workshop


When Sep 11, 2009 - Sep 11, 2009
Where Bled, Slovenia
Submission Deadline Jun 10, 2009
Notification Due Jun 30, 2009
Final Version Due Aug 1, 2009
Categories    knowledge discovery   data mining

Call For Papers

KDPM'09: Knowledge Discovery Meets Process Mining Workshop
at ECML/PKDD 2009
Bled, Slovenia, September 7 - 11, 2009

Within the Business Process Intelligence community, there is a
large and lively sub-community, the process mining one. Process
mining targets the discovery of information based on event
logs. For instance, the automatic discovery of process models
from event logs. Examples of event logs include process data
generated by administrative services, health care data about
patient handling, and logs of workflow tools. Many machine
learning and data mining techniques have successfully been
applied in this field. Nevertheless, the process mining
community and the mainstream data mining community have
remained relatively disconnected. Only few papers on process
mining appeared in data mining or machine learning conferences,
even though many of the research issues fit equally well in
both communities.

With this workshop we want to strengthen the ties between the
two research communities. ECML/PKDD is an excellent venue for
this: both the machine learning and data mining community are
present, and process mining matches well with the variety of
topics covered in these conferences. On the one hand, process
mining is a challenging research area that has the potential of
becoming as important as more traditional themes, such as graph
or sequence mining. On the other hand, process mining can
benefit from the input of related fields in data mining and
machine learning. The most obvious candidates from DM and ML
side for cross-fertilization are those fields involved with
finding patterns that are temporal or sequential in nature,
such as temporal data mining, sequence and episode mining,
web-log mining. Also the mining of structured patterns such as
graphs and partial orders are clearly related. Some examples of
typical problems in the process mining field where data mining
or machine learning techniques can make the difference are in
the handling of noisy data, scalability, and the discovery of
less structured and hierarchical models.

Contributions are sought in all areas related to the discovery
of structure and regularities in event/process driven data. A
non-exhaustive list of topics: - The discovery of structured
process models such as Petri-nets from event data; - Modelling
techniques for describing the structure of event data such as
Markov Models; - Scalable and robust process mining algorithms
and techniques; - Process mining evaluation: metrics,
approaches and frameworks; - Integration of domain knowledge in
process mining; - Adaptation of web mining, text mining,
temporal data mining approaches for process mining needs; -
Lessons learnt from (un)successful process mining case studies.
- Please note that we do not want to focus the workshop solely
on the discovery of process models, but are open to all
research related to finding regularities and patterns in
process oriented data.

Authors are invited to submit original and unpublished
manuscripts (LNCS, 12 pages maximum) to by
June 10. Submitted papers will undergo a peer-reviewing
process. Final versions of accepted papers will appear in the
informal ECML/PKDD workshop proceedings. Submission implies the
willingness of at least one of the authors to register and
present the paper. We also intend to organize a post-workshop
publication in the form of LNCS proceeding or a special issue
in a journal.

June 10th, 2009 Paper submission deadline (12 LNCS pages max)
June 30th, 2009 Notification of acceptance
August 1st, 2009 Final camera-ready paper due
September 11, 2009 KDPM'09 Workshop day

Worshop Chairs:
Toon Calders, Mykola Pechenizkiy, Boudewijn van Dongen
Eindhoven University of Technology, the Netherlands

Programme Committee:
see KDMP'09 website

For further questions please contact us at

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