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<div><font size="2">========================================================</font></div>
<div><font size="2">Call for Papers: </font><font size="2">2010 IEEE International Workshop on Data Mining for Service (DMS<a></a><a></a> '10)</font></div>
<div><font size="2"></font> </div>
<div><font size="2">In conjunction with the 10th IEEE International Conference on Data Mining (ICDM<a></a><a></a> '10)</font></div>
<div><font size="2"></font> </div>
<div><font size="2">December 14, 2010, Sydney, Australia</font></div>
<div><font size="2"></font> </div>
<div><font size="2">Workshop website: </font><a href="http://www2.ipcku.kansai-u.ac.jp/~yada/conf/dms10/"><font size="2">http://www2.ipcku.kansai-u.ac.jp/~yada/conf/dms10/</font></a></div>
<div><font size="2">========================================================</font><font size="2"></font></div>
<div><strong><font size="2"></font></strong> </div>
<div><strong><font size="2">Important Dates</font></strong></div>
<div> </div>
<div><font size="2">Submissions due: July 23, 2010<br>
Notifications of acceptance: September 20, 2010<br>
Camera-ready paper due: October 11, 2010<br>
Workshop day: December 14, 2010</font></div>
<div><font size="2"></font> </div>
<div><strong><font size="2">Scope</font></strong></div>
<div> </div>
<div><font size="2">In midst of service applications in engineering and the increasing importance of the service sector in the global economy, services are being scientific and much attention is being focused on service science as a means to improve productivity.
Since services are amorphous (they have no shape) and have the special characteristic of simultaneously causing both production and consumption, it has been difficult to research services in a scientific way. However recently, due to the spread of the internet
and technical innovations in sensor networks, huge amounts of data related to all kinds of service activities and processes are being collected, and a new frontier of service research is starting to appear. Given this background, data mining, which can uncover
useful knowledge from such masses of data, is expected to take an important role in the development of service science. The focus of this workshop is on empirical findings, methodological papers, and theoretical and conceptual insights related to data mining
in the field of various service application areas.</font></div>
<div><font size="2"></font> </div>
<div><strong><font size="2">Topics of Interest</font></strong></div>
<div> </div>
<div><font size="2">The workshop is aimed at bringing together researchers from the areas of the service sector and data mining. We expect to encourage an exchange of ideas and perceptions through the workshop, focused on service and data mining. Possible topics
of interest include, but are not limited to:</font></div>
<p><font size="2"></font> </p>
<p><font size="2">- Information systems for service to understand consumer behavior</font></p>
<p><font size="2">- Information systems to integrate various services</font></p>
<p><font size="2">- New data mining applications and new insights for service science</font></p>
<p><font size="2">- Case studies of data mining applications for service science</font></p>
<p><font size="2"></font> </p>
<p><font size="2">We are interested in the emergence of new business systems in the real business world, and encouraging new applications of data mining in service science. Therefore, submitted papers will be evaluated from the perspectives of traditional criteria
such as technical originality and prediction accuracy, while also going beyond to consider creativity and applicability. Case studies that include successes and failures in service science are also welcome.</font></p>
<p><font size="2"></font> </p>
<p><font size="2">Technical issues include (but not limited to):</font></p>
<p><font size="2"></font> </p>
<p><font size="2">Data Mining</font></p>
<p><font size="2">- machine learning algorithms and methods</font></p>
<p><font size="2">- text and semi-structured data mining</font></p>
<p><font size="2">- pattern recognition</font></p>
<p><font size="2">- knowledge representation</font></p>
<p><font size="2">- statistics and probability</font></p>
<dl><dt><font size="2">Areas of Interest</font> </dt><dt><font size="2">- marketing</font> </dt><dt><font size="2">- corporate strategy</font> </dt><dt><font size="2">- finance</font> </dt><dt><font size="2">- medicine</font> </dt><dt><font size="2">- nursing care</font> </dt><dt><font size="2"></font> </dt><dt><font size="2">Examples in Marketing</font> </dt><dt><font size="2">- marketing science</font> </dt><dt><font size="2">- consumer behavior</font> </dt><dt><font size="2">- retailing and pricing</font> </dt><dt><font size="2">- advertising</font> </dt><dt><font size="2">- customer relationship management</font> </dt><dt><font size="2">- brand management</font> </dt><dt><font size="2">- innovation</font></dt></dl>
<p><strong><font size="2">Workshop Organizers</font></strong></p>
<p> </p>
<p><font size="2"><a></a><font size="2"><a></a></font><font size="2"><font size="2">Katsutoshi</font></font> Yada<a></a><a></a>, Kansai University, Japan (Contact Person)<br>
Yukio Ohsawa<a></a><a></a>, University of Tokyo, Japan<br>
Russ Winer<a></a><a></a>, New York University, USA</font></p>
<p><font size="2"></font> </p>
<p><font size="2"></font> </p>
<div><font size="2">========================================</font></div>
<div><font size="2"><em>H. Michelle Chen</em></font></div>
<div><font size="2"><em>Assistant Professor of Operations & Information Management</em></font></div>
<div><font size="2"><em>School of Business, University of Connecticut</em></font></div>
<div><font size="2"><em>2100 Hillside Rd U1041<a></a><a></a>, Storrs, CT 06269, USA</em></font></div>
<div><font size="2"><em>Email: </em><em><em><em><a href="mailto:michelle.chen@business.uconn.edu"><em>michelle.chen@business.uconn.edu</em></a></em><a></a></em><a></a></em></font></div>
<div><font size="2"><em>Office: +1 (860) 486-6115</em></font></div>
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