Workshop on Data Mining in Industrial Internet of Things (DMIIOT)
to be held in conjunction with the IEEE International Conference on Data Mining 2019 in Beijing
Data generated by industrial internet of things (IIOT) have been growing at an exponential rate. Data mining plays an essential role in deriving actionable information from these raw data. By applying a variety of data mining technologies to historical and real time IIOT data, building supervised or unsupervised models, deploying them into the production environment to help business make better decisions, significant value can be created resulting in reduced waste, improved efficiency and broaden opportunity. The marriage between data mining and IIOT has found applications in industries such as manufacturing, energy, healthcare, retail, smart city and transportation.
The workshop will provide a venue for researchers and practitioners from both data mining and IIOT communities to exchange ideas, share best practices, discuss challenges and future directions. By fostering communication and collaboration, we drive innovative applications of data mining to IIOT. This workshop will be held along with 2019 IEEE International Conference on Data Mining, Beijing (http://icdm2019.bigke.org).
Schedule
The workshop is scheduled on the morning of Nov 8 2019. Due to the room limitation, DMIIOT'19 and OEDM'19 (Workshop on Optimization Based Techniques for Emerging Data Mining Problems) have merged under the name "OEDM & DMIIOT". The detailed information about OEDM can be found on the workshop website. The accepted papers of both workshops will be presented alternately and we believe this will bring new thoughts to attendees from both workshops.
Workshop time: 08:00-11:50, Nov 8, 2019 Workshop Room: TBD Presentation schedule: There are 5 accepted papers from OEDM'19 and DMIIOT'19 together, and the detailed schedule is as follows. 08:00-08:40 Paper ID: S10201 Authors: Yuhan Lin, Minglong Lei, and Lingfeng Niu Title: Optimization Strategies in Quantized Neural Networks: A Review 08:40-09:20 Paper ID: S15201 Authors: Tomonari Masada, Takumi Eguchi, and Daisuke Hamaguchi Title: Difference between Similars: a Novel Method to Use Topic Models for Sensor Data Analysis 09:20-10:00 Paper ID: S10203 Authors: Kazuki Koyama, Keisuke Kiritoshi, and Tomonori Izumitani Title: Discovering Sparse and Ununiform Lag Structure Using VAR Models with Latent Group LASSO 09:20-10:00: Coffee Break 10:30-11:10 Paper ID: S15202 Authors: Devon Peticolas, Russell Kirmayer, and Deepak S. Turaga Title: M´ımir: Building and Deploying an ML Framework for Industrial IoT 11:10-11:50 Paper ID: S10205 Authors: Xing Nie, Yang Hu, Guoliang Ma, and Fanhua Shang Title: RASVRG: Robust Accelerated Stochastic Variance Reduction Gradient for Sparse Subspace Clustering
Steering Committee
Program Committee
Workshop Chairs
E-mail: ping.chen@umb.edu; Website: http://www.cs.umb.edu/~pchen
E-mail: jay.zhou@aistrike.us; Website: http://aistrike.us
Media Partners