The 4th International Workshop on Big Data Analysis for Smart Energy
(BigData4SmartEnergy 2020)

CALL FOR PAPERS

The 4th International Workshop on Big Data Analysis for Smart Energy (BigData4SmartEnergy 2020)

November 17, 2020, Sorrento, Italy

In conjunction with the ICDM 2020: 20th IEEE International Conference on Data Mining

Purpose and Scope

AI-based smart energy deals with the challenges of using big data and AI techniques for the efficient management of energy resources and facilities, the timely detection of energy problems and anomalies, and the effective prediction of energy demands and services in the future, hence has already become an important area of research in many countries. This workshop aims to create opportunities to share on-going works in smart energy industry and academia, to enhance collaboration between energy researchers and data scientists, and to foster new innovations of AI, big data, and smart computing technologies in such areas as smart energy platforms and infrastructure, monitoring and analysis of energy big data, customized demand forecast in smart cities, smart homes, and electric vehicles, new innovative energy services, and so on. Prospective authors are cordially invited to submit their original contributions covering completed or ongoing work in (but not limited to) the following areas:

  • Innovative AI techniques for future energy services and monitoring
  • Intelligent techniques for smart energy platforms and infrastructure
  • Big data analytics for smart energy operations, measurement and control
  • Real-time handling and storage of stream data for power facilities
  • Intelligent monitoring and real-time analysis of power energy big data
  • Data mining and metadata annotation of power energy big data
  • Big data- and/or block chain-based security systems for energy services
  • Evaluation, monitoring and visualization techniques for energy big data
  • Intelligent asset management of transmission and distribution facilities
  • Machine learning techniques for anomaly detection in power services
  • Electricity demand prediction for smart homes and smart cities
  • Smart grid and customized energy services demand prediction
  • Energy ecosystems and related issues

PAPER SUBMISSION

Prospective authors are invited to submit their papers, up to 8 pages plus 2 extra pages in English and in PDF according to the IEEE two-column format for conference proceedings, through the submission site link below. All submissions will be peer-reviewed by the Program Committees of the workshop.
Furthermore, as in previous years, papers that are not accepted by the ICDM main conference will be automatically sent to a workshop selected by the authors when the papers were submitted to the main conference. By the unique ICDM tradition, all accepted workshop papers will be published in the dedicated ICDMW proceedings published by the IEEE Computer Society Press.

Direct link for paper submission:
https://wi-lab.com/cyberchair/2020/icdm20/scripts/ws_submit.php?subarea=S

Paper templates:
https://www.ieee.org/conferences/publishing/templates.html


IMPORTANT DATES

All deadlines are at 11:59PM Central Europe Time on the stated date.

Paper submission due
August 24, 2020 August 31, 2020
Acceptance notification
September 17, 2020
Camera-ready copies due
September 24, 2020 September 27, 2020
Author registration due
September 29, 2020
Workshop date
November 17, 2020

ORGANIZING COMMITTEE

General Chair

  • Ho-Jin Choi, KAIST, Korea

Program Co-Chairs

  • Sung-Bae Cho, Yonsei University, Korea
  • Eenjun Hwang, Korea University, Korea
  • Kyuchul Lee, Chungnam National University, Korea
  • Yang-Sae Moon, Kangwon National University, Korea
  • Chan-Hyun Youn, KAIST, Korea

Program Committee

  • Hoon Choi, Chungnam National University, Korea
  • Jun Kyun Choi, KAIST, Korea
  • Mi-Jung Choi, Kangwon National University, Korea
  • Jaegul Choo, Korea University, Korea
  • Youssef Iraqi, Khalifa University, UAE
  • Young-Seob Jeong, SoonChunHyang University, Korea
  • Pilsung Kang, Korea University, Korea
  • Jinho Kim, Kangwon National University, Korea
  • Myoung Ho Kim, KAIST, Korea
  • Seung Wan Kim, Chungnam National University, Korea
  • Chang-Ki Lee, Kangwon National University, Korea
  • Kyong-Ho Lee, Yonsei University, Korea
  • Tae-Eog Lee, KAIST, Korea
  • Zhu Lingyun, Chongqing University of Technology, China
  • Loubna Mekouar, Zayed University, UAE
  • Cheong Hee Park, Chungnam National University, Korea
  • Jaecheol Ryou, Chungnam National University, Korea
  • Seungwon Shin, KAIST, Korea
  • Yong-June Shin, Yonsei University, Korea
  • Yong Wang, Chongqing University of Technology, China
  • Nan Xiang, Chongqing University of Technology, China
  • Chan Yeob Yeun, Khalifa University, UAE
  • Seyoung Yun, KAIST, Korea

KEYNOTE SPEECH

Scalable Distributed Pivot Analysis over Massive Big Data: Models, Paradigms, New Advancements

Speaker

Alfredo Cuzzocrea
Alfredo Cuzzocrea
  • Professor, University of Calabria

Bio

Alfredo Cuzzocrea is Associate Professor in Computer Engineering at the DISPES Department – Section: Management Information Systems of University of Calabria, Italy. He also holds the Excellence Chair in Computer Engineering – Big Data Management and Analytics at the LORIA Lab of the University of Lorraine, Nancy, France. On June 2019, he has been awarded as Full Professor in Computer Engineering at the Department of Computer Science of University of Bourgogne Franche-Comte, Besancon, France. He is habilitated as Full Professor in Computer Engineering and Full Professor in Computer Science by the Ministry of Education, University and Research (MIUR), Italy, and Full Professor in Computer Science by the Ministry of Higher Education and Research (MESR), France. (A full biography can be found HERE.)


PROGRAM (tentative)

※ All programs are UTC±0 time zone. Please make sure the time zone corresponding to your location.

UTC 12:00-12:45, November 17 (Tuesday), 2020

Live Keynote Session

Scalable Distributed Pivot Analysis over Massive Big Data: Models, Paradigms, New Advancements
Alfredo Cuzzocrea (University of Calabria, Italy)

UTC 13:00-13:42, November 17 (Tuesday), 2020

Panel Session 1   - Energy Forecasting and Anomaly Prediction

13:00-13:07
Data Analysis and Processing for Spatio-temporal Forecasting
Hyoungwoo Lee and Jaegul Choo (Korea University, Korea)
13:07-13:14
Explainable Anomaly Detection for District Heating Based on Shapley Additive Explanations
Sungwoo Park, Jihoon Moon, and Eenjun Hwang (Korea University, Korea)
13:14-13:21
Precipitation Nowcasting Using Grid-based Data in South Korea Region
ChangHwan Kim and Se-Young Yun (KAIST, Korea)
13:21-13:28
Anomaly Detection and Visualization for Electricity Consumption Data
Nyoungwoo Lee, Jehyun Nam, and Ho-Jin Choi (KAIST, Korea)
13:28-13:35
StreamDL: Deep Learning Serving Platform for AMI Stream Forecasting
Eunju Yang, Changha Lee, Ji-Hwan Kim, Tuan Manh Tao, and Chan-Hyun Youn (KAIST, Korea)
13:35-13:42
Electric Energy Demand Forecasting with Explainable Time-series Modeling
Jin-Young Kim and Sung-Bae Cho (Yonsei University, Korea)

UTC 14:00-14:42, November 17 (Tuesday), 2020

Panel Session 2   - Smart Energy Famework, Model Design, and Applications

14:00-14:07
User Authentication Method using FIDO based Password Management for Smart Energy Environment
Hyunjin Kim, Dongseop Lee, and Jaecheol Ryou (Chungnam National University, Korea)
14:07-14:14
DQN-based Join Order Optimization by Learning Experiences of Running Queries on Spark SQL
Kyeong-Min Lee, InA Kim, and Kyu-Chul Lee (Chungnam National University, Korea)
14:14-14:21
SIPA: A Simple Framework for Efficient Networks
Gihun Lee, Sangmin Bae, Jaehoon Oh, and Se-Young Yun (KAIST, Korea)
14:21-14:28
An Accelerated Continual Learning with Demand Prediction based Scheduling in Edge-Cloud Computing
Changha Lee, Seong-Hwan Kim, and Chan-Hyun Youn (KAIST, Korea)
14:28-14:35
Design of Neural Network-based Boost Charging for Reducing the Charging Time of Li-ion Battery
Sue Hyang Lim, Seon Hyeog Kim, Hyeong Min Lee, Si Joong Kim, and Yong-June Shin (Yonsei University, Korea)
14:35-14:42
Learning Disentangled Representation of Residential Power Demand Peak via Convolutional-recurrent Triplet Network
Hyung-Jun Moon, Seok-Jun Bu, and Sung-Bae Cho (Yonsei University, Korea)

CONTACT

For any inquiries, please contact: