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| {{Event
| | Articels like this make life so much simpler. |
| | Acronym = IPM 2008
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| | Title = The 2nd International Workshop on the Induction of Process Models
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| | Type = Workshop
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| | Series =
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| | Field = Computer security and reliability
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| | Homepage = wwwkramer.in.tum.de/ipm08
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| | Start date = Sep 15, 2008
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| | End date = Sep 15, 2008
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| | City= Antwerp
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| | State =
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| | Country = Belgium
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| | Abstract deadline =
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| | Submission deadline = Jun 16, 2008
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| | Notification = Jun 30, 2008
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| | Camera ready =
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| }}
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| <pre>
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| The 2nd International Workshop on the Induction of Process Models
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| (IPM?08) at ECML PKDD 2008, 15 September 2008, Antwerp, Belgium
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| http://wwwkramer.in.tum.de/ipm08/
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| * Call for Abstracts (deadline June 16th)
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| While the worlds of science and business typically meet in the
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| presence of a profitable scheme, individuals from both environments
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| have interests in analyzing complex data about dynamic systems.
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| Whether motivated by a drive to increase system efficiency or to
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| understand nature, their shared goal leads to a shared focus on the
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| underlying causal processes that explain or produce observed
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| phenomena. To this end, researchers construct models from data
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| derived from observed system behavior and background knowledge about
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| the candidate processes. Traditional literature on regression,
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| time-series analysis, and data mining produces descriptive models
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| that may reproduce the observed data but cannot explain the
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| principal dynamics. Therefore, researchers are called to develop
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| methods that capture complex temporal and spatial relationships in
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| terms of domain knowledge (e.g., relevant scientific or business
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| concepts) and that construct these explanatory process models.
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| One can develop both qualitative and quantitative process models
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| depending on their intended use. Qualitative approaches to model
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| induction include learning state transition models, Petri-nets, and
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| learning from (time-stamped) event sequences and event logs.
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| Qualitative representations are particularly interesting for
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| business applications that aim to discover business processes from
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| data. Examples of event logs include process data generated by
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| administrative services, health care data about patient handling,
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| and logs of workflow tools. In comparison, quantitative approaches
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| to model construction are grounded in standard mathematical
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| representations (e.g., systems of differential equations).
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| Quantitative representations are common in scientific applications,
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| and are especially prominent in the environmental and biological
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| sciences that deal with complex, natural systems. Notably, the
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| business and scientific worlds are not separated by an interest in
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| the qualitative or quantitative emphasis of their models. Moreover,
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| researchers working in these domains would benefit from approaches
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| that integrate the qualitative and quantitative aspects of system
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| behavior.
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| In this workshop, we aim to attract researchers with an interest in
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| inductive process modeling in different formalisms including Petri
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| nets, qualitative and quantitative processes, differential
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| equations, episode rules, logical rules, and others. Also, although
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| we have emphasized the business and scientific domains, we are open
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| to any application of process model induction. A non-exhaustive list
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| of topics includes:
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| - learning structured process models such as Petri net or process
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| algebra models from event logs
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| - modeling techniques for describing the structure of event data
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| such as Markov models
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| - learning differential equation models
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| - learning in qualitative reasoning representations learning in
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| temporal logic
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| - learning logical models of state transitions (e.g., by recursive
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| clauses)
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| - learning from time-stamped event sequences (e.g., episode rules)
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| - learning from large databases of trajectories
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| - connectionist/subsymbolic models of sequence learning
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| - scalable and robust process mining algorithms and techniques
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| - process mining evaluation: metrics, approaches and frameworks
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| - the adaption of web mining, text mining, temporal data mining
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| approaches for inductive process modeling
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| Particularly welcome are case studies and applications (e.g., from
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| business, the environmental, medical or biological sciences) and
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| discussions of the lessons learned from such case studies and papers
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| identifying open problems such as dealing with missing and/or noisy
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| data, regularization, incorporating background/domain knowledge,
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| efficient search through the space of candidate process-based
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| models, ... Inductive process modeling and process mining are
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| challenging research areas that have the potential to grow in
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| importance like graph or sequence mining. On the other hand, process
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| mining can benefit from the input of related fields in data mining
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| and machine learning, such as temporal data mining, episodes and web
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| log mining. In the ECML/PKDD 2008 workshop on the induction of
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| process models, we intend to bring scientists together and actively
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| identify common research threads, define open problems, and develop
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| collaborative contacts. It should provide a more relaxed atmosphere
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| than a conference setting where participants are encouraged to ask
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| clarifying questions throughout the talks and to move past
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| jargon-induced barriers.
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| * Submission
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| Extended abstracts (two pages in Springer format) should be
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| submitted by June 16th, 2008 by email to ipm08@in.tum.de . Final
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| versions of accepted papers will appear in the informal ECML/PKDD
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| workshop proceedings and will be made available on the workshop
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| website before the workshop takes place. Submission implies the
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| willingness of at least one of the authors to register and present
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| the paper. Authors of accepted abstracts will be asked to submit a
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| short 4 to 8 page paper in PDF format (following the Springer LNCS
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| guidelines for preparing manuscripts) that describes their research
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| in more detail.
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| * Important Dates
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| Abstracts due June 16th
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| Author Notification on June 30th
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| Final Papers due August 4th
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| Workshop September 15th
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| * Organizing Committee
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| Will Bridewell, Stanford University, USA
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| Toon Calders, Eindhoven University of Technology, The Netherlands
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| Ana Karla de Medeiros, Eindhoven University of Technology, The Netherlands
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| Stefan Kramer, Technische Universit?t M?nchen, Germany
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| Mykola Pechenizkiy, Eindhoven University of Technology, The Netherlands
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| Ljupco Todorovski, University of Ljubljana, Slovenia
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| </pre>This CfP was obtained from [http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=3190&copyownerid=2 WikiCFP]
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