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Workshop on Data Mining for Service and Maintenance (KDD4Service)

From: Prem Melville <publicity@icml-2011.org>
Date: Tue, 26 Apr 2011 18:53:23 -0400
Message-ID: <BANLkTinbmTuo1gca=wTCpSpDJRVGVb6BJQ@mail.gmail.com>
To: undisclosed-recipients:;

Workshop on Data Mining for Service and Maintenance (KDD4Service)
August 21, 2011 - San Diego, CA, USA

in conjunction with SIGKDD 2011 (http://www.sigkdd.org/kdd2011/)

System (or equipment, machine, instrument) maintenance or servicing
is a crucial part of business for many industrial processes,
especially high-cost and safety-critical processes such as power
plant, oil/gas turbine or aircraft engine operations. Instead of the
common practice of fixing the maintenance schedule for the equipment
(e.g., by usage time) or doing reactive diagnostics maintenance
(i.e., fixing the problem after it happens), increasingly industries
are moving toward a so-called proactive service mode (which is often
called condition monitoring, preventive maintenance, among other
names). The idea is to monitor the system closely via service data
analytics, and schedule maintenance timely (e.g., when some
components need to be serviced or replaced) and proactively (before
the system is down). Service-data analytics is a challenging task
due to several intrinsic characteristics of modern industrial
systems. First, systems like aircraft engines are very complex and
the measurable variables are far less than enough to model system
dynamics. Secondly, one often observes significant system-to-system
variances, due to various initial conditions, operation modes and
operation environments. Thirdly, for a system composed of tens of
thousands of components, there exist multiple potential failure
modes, which present different signatures in the data. Last but not
the least, missing data is common in service data because of
practical constraints.

The purpose of this multi-disciplinary workshop is to bring together
industry experts and researchers from data mining, machine learning,
text analysis and signal processing who share an interest in
problems and applications of system service and maintenance. The
goal will be to formulate the relevant research questions and bridge
the gap between data mining research and industry needs in this
important field. We plan to provide a platform for exchange of
ideas, identification of important and challenging applications, and
discovery of possible synergies. The difference between service and
maintenance will be highlighted in multiple industries. It is our
hope that this will spur vigorous discussions and encourage
collaboration between the various disciplines, potentially resulting
in collaborative projects. We will particularly emphasize the
mathematical and engineering aspects of service data analysis. We
are planning a potential Data Mining & Knowledge Discovery or
IEEE-KDE special journal issue, or an edited book in 2012-2013.

Topics of interest include but are not limited to:
- Time series and data stream classification for prognostics
- Feature extraction from time series and semi-structured texts
- Prognostics modeling
- Survival analysis
- Regression and ranking from time series
- De-noising and handling missing data in service data
- Rule based systems for service
- Combining multiple data sources for prediction
- Classification with imperfect class labels (e.g., noisy service notifications)
- Performance measures for preventive maintenance
- Knowledge representation for service
- Risk-sensitive data mining based decision support for prognostics tasks
- Empirical preventive maintenance data sets and comparison
- Testing and validation of data mining algorithms for service
- Cost-sensitive data mining for service and maintenance
- Other service applications

Important Dates
- Submission deadline: May 27th, 2011
- Notification of acceptance: June 10th, 2011
- Final papers due: June 17th, 2011

Full paper submission (oral presentation at the workshop) should
have a maximum length of 4 pages in content plus 1 page reference.
Position papers and posters are also welcome. Authors are required
to use the ACM camera ready template
(http://www.acm.org/sigs/pubs/proceed/template.html) and submit the
PDF version via the EasyChair system (link will be available later
on the workshop website). All submissions should clearly state the
author information including their names, affiliations and emails.

Workshop Organizers
- Shipeng Yu, Siemens Healthcare
- Ya Xue, GE Global Research
- Dragos Margineantu, Boeing Research and Technology
- Nikunj C. Oza, NASA Ames Research Center
- Michal Rosen-Zvi, IBM Haifa Research Lab
- R. Bharat Rao, Siemens Healthcare
Received on Tuesday, 26 April 2011 22:53:52 UTC

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