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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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| − | | |
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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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