LMID 2019

Workshop on Learning and Mining with Industrial Data

WORKSHOP

Workshop on Learning and Mining with Industrial Data (LMID 2019) will be held in conjunction with the 2019 IEEE International Conference on Data Mining (ICDM 2019) on November 8-11. The conference/workshop venue is in Beijing, China.

INTRODUCTION

Digital technologies, the Internet of Things (IoT), cloud computing, and edge computing are transforming manufacturing and industry. Machine learning and data mining on industrial data have crucial impact on optimizing all aspects in the manufacturing process, including design, engineering, manufacturing, supply chain, and services. This research field also brings some challenges for learning methods, such as interconnected sensor data, real-time learning, multimodal data analysis, and resource-constrained devices. This workshop aims to bring together researchers and practitioners from academia and industry to discuss challenges, emerging topics, and recent advances in learning and mining with industrial data.

TOPICS

We encourage submissions on theory, methods, and applications on various aspects in industrial data analysis. Topics of interest include, but are not limited to:

SUBMISSION & PUBLICATION

All submissions should be between four (4) and eight (8) pages, and follow the IEEE ICDM format (see the IEEE ICDM 2019 Submission Guidelines for more details). Please submit your manuscript through the ICDM 2019 submission site.

All accepted submissions will be included in the IEEE ICDM 2019 Workshops Proceedings published by IEEE Computer Society Press, and will be also included in the IEEE Computer Society Digital Library (CSDL) and IEEE Xplore (indexed by EI).

IMPORTANT DATES

Paper submission: August 7, 2019
Paper notification: September 4, 2019
Camera-ready deadline and copyright forms: September 8, 2019
Workshop date: November 8-11, 2019

INVITED SPEAKERS

TBA

PROGRAM

TBA

WORKSHOP ORGANIZATION

Organization Committee


Program Committee

CONTACT US

Email:

ACKNOWLEDGEMENT

The organizers would like to acknowledge the support provided by the EU Marie Skłodowska-Curie Actions (MSCA) project ECOLE.

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