LMID 2019

Workshop on Learning and Mining with Industrial Data


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. The conference/workshop venue is in Beijing, China.


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.


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


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). All submissions will be triple-blind reviewed on the basis of the ICDM review policy. 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).


Paper submission: August 18, 2019
Paper notification: September 4, 2019
Camera-ready deadline and copyright forms: September 8, 2019
Workshop date: November 8 2019, 13:00-18:00






Keynote: Automated composition, optimisation and adaptation of complex predictive systems
 Bogdan Gabrys


Scoring Message Stream Anomalies in Railway Communication Systems
 Lucas Foulon, Serge Fenet, Christophe Rigotti, and Denis Jouvin

Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection
 Naoya Takeishi

Automated Machine Learning Techniques in Prognostics of Railway Track Defects
 Simon Kocbek and Bogdan Gabrys

Data Mining in Railway Defect Image Based on Object Detection Technology
 Bing Zhao, Mingrui Dai, Ping Li, Xiaoning Ma


Coffee Break


Keynote: TBA
 Xin Yao


Identifying Topological Prototypes using Deep Point Cloud Autoencoder Networks
 Nidhi Nivesh Dommaraju, Mariusz Bujny, Stefan Menzel, Markus Olhofer, and Fabian Duddeck

Data-driven insights from predictive analytics on heterogeneous experimental data of industrial magnetic materials
 Zijiang Yang, Tetsushi Watari, Daisuke Ichigozaki, Kei Morohoshi, Yoshinori Suga, Wei-keng Liao, Alok Choudhary, and Ankit Agrawal

Learning Time-series Data of Industrial Design Optimization using Recurrent Neural Networks
 Sneha Saha, Thiago Rios, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck, Xin Yao, Zhao Xu, and Patricia Wollstadt

A Dynamically Adaptive Movie Occupancy Forecasting System with Feature Optimization
 Sundararaman Venkataramani, Ateendra Ramesh, Sharan Sundar S, Aashish Kumar Jain, Gautham Krishna Gudur, and Vineeth Vijayaraghavan




Organization Committee

Program Committee


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

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