WORKSHOP

IEEE International Workshop on Data Mining for Service (DMS 2026) will be held in conjunction with
The 2026 IEEE International Conference on Data Mining (ICDM'2026)on November 12-15, 2026.
The confenrence/workshop venue will be presented in Shenyang, China.
DMS workshop will be held on November 12nd.
28 papers were submitted and 12 papers were accepted by strict review.
Thank you for your contribution!!

Workshop Title: International Workshop on Data Mining for Service (DMS 2026)
Workshop Number (Schedule ID): W34
Date: October 12, 15:50-18:50 (Local Time)
Room number and venue: London Room

SCOPE

The International Workshop on Data Mining for Service (DMS 2026) provides a forum for researchers and practitioners to discuss recent advances in data mining, machine learning, artificial intelligence, and analytics for service systems. The workshop focuses on the development of data-driven methods that support decision making, service design, customer experience management, healthcare, retail, finance, smart cities, and other service-oriented domains. As digital transformation generates increasingly rich and diverse data, new opportunities emerge to discover actionable knowledge and create value through advanced analytical techniques. DMS 2026 welcomes research on novel algorithms, real-world applications, multimodal data analysis, explainable AI, predictive modeling, and intelligent service systems. The workshop aims to promote collaboration between academia and industry and to advance both methodological innovation and practical impact in service science.

TOPICS

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:

* Information systems for service to understand consumer behavior
* Information systems to integrate various services
* New data mining applications and new insights for service science
* Data-oriented service innovation
* Case studies of data mining applications for service science

Especially, this year, we focus on the topics related to Big Data in the workshop.
This workshop will discuss specific uses of data mining techniques for Big Data to create new service.
Possible topics of interest include below:

* New service and Big Data
* Novel model and Big Data
* Any service application of data mining using Big Data

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.

Data Mining

Areas of Interest

Examples in Marketing

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SUBMISSION & PUBLICATION

Paper submissions should be limited to a maximum of 8 pages, and follow the IEEE ICDM format. https://www.ieee.org/conferences/publishing/templates

Please submit your manuscript through the DMS 2026 submission site (No.17).

All accepted papers will be included in the ICDM'26 Workshop Proceedings published by the IEEE Computer Society Press. Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals.

All accepted papers, including workshops, must have at least one “FULL” registration. A full registration is either a “member” or “non-member” registration. Student registrations are not considered full registrations. All authors are required to register by 5th October 2026.

IMPORTANT DATE

Submissions due: August 20, 2026
Notifications of Acceptance: September 20, 2026
Camera-ready deadline and copyright forms: October 4, 2026 (NEW)
Workshop day: November 12(TBA), 2026.

WORKSHOP ORGANIZATION

Workshop-Chair

LINKs

Sponsors

Past Workshops

CONTACT US

Katsutoshi Yada
Faculty of Business Data Science, Kansai University, Japan
Email: yada@kansai-u.ac.jp

Shusaku Tsumoto
Department of Medical Informatics, Faculty of Medicine, Shimane University, Japan
Email: tsumoto@med.shimane-u.ac.jp

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