DMS 2012 - IEEE International Workshop on Data Mining for Service

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.
TOPICS
The workshop is aimed at bringing together researchers from the areas of the ice 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:
Information systems for service to understand consumer behavior
Information systems to integrate various services
New data mining applications and new insights for service science
Case studies of data mining applications for service science
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.
Technical issues include (but not limited to)
Data Mining
machine learning algorithms and methods
text and semi-structured data mining
pattern recognition
knowledge representation
statistics and probability
Areas of Interest
marketing
corporate strategy
finance
medicine
nursing care
Examples in Marketing
marketing science
consumer behavior
retailing and pricing
advertising
customer relationship management
brand management
innovation