Big Data Analytics for Sustainability (BDA4S) 2022

A Workshop at 2022 IEEE International Conference on Big Data (IEEE Big Data 2022)

December 17th - 20th, 2022, Osaka, Japan

Due to numerous requests the deadline has been extended to 28/10/2022. Reviews will come in batches.

Outline

Sustainability is a significant challenge, given the impending consequences of global warming. Computing has a crucial role to play in ensuring that we develop sustainable interactions with our environment and tackling the challenges associated with mitigating climate change. Big Data is a key asset in developing a more sustainable environment and can lead to new breakthroughs and better use of the resources we have. However, computing can be seen as part of the solution, as we use computational resources to make the world a more sustainable place, but also part of the problem – with ICT accounting for 1.11Gt of CO2 in 2020 and estimated to more than double by 2030.

This workshop will focus on the cutting-edge developments from both academia and industry, with a particular emphasis on novel techniques to capture, store and process big data from a wide range of sources for improving sustainability, and in particular on the methodologies and technologies which can be applied to correlate, learn and mine, interpret and visualise data which will lead to a more healthy, and sustainable, interactions with the environment.

This workshop is timely and interesting for researchers, academics and practitioners in big data processing and analytics, energy efficiency, sustainability, and Green Computing. The workshop is very relevant to the big data community, especially data mining, machine learning, cyber-physical systems, and computational intelligence. It will bring forth a lively forum on this exciting and challenging area at the conference.

Previous workshops

Research Topics

The workshop only considers well-written manuscripts that describe original, unpublished, state-of-the-art research and practical work. Indicative topics for the workshop are as follows:

Sustainability of Computing

• Big data analytics for sustainable computing
• Data mining and machine learning for sustainable computing
• Decision support for computer energy management
• Lifecycle management of computing resources
• Reduction of resource requirements for computational work
• Efficient use of Cloud resources
• Efficient use of local resources
• Comparisons of energy usage of computational equipment
• Estimation of energy consumption
• Evaluation of techniques to reduce consumption

Computing for Sustainability

• Using Big Data Analytics to improve sustainability
• Visualisation of sustainability
• Decision support through computer analysis
• Lifecycle management through big data and/or analytics
• Digital twin analytics
• Big Data policy analytics
• Methane emission tracking and reduction
• Food chain and systems optimisation
• Mobility and transport analytics
• Sustainable and resilient built infrastructure in urban areas
• Power and energy systems
• Climate modelling
• Climate finance
• Air quality
• Public health and the environment

To contribute toward advances of knowledge, the workshop will solicit submissions of manuscripts from researchers and practitioners who are actively working in Big Data Analytics for Sustainability.

Paper Format

Papers should be formatted using the two column IEEE CS template and can be up to 10 pages (including references) in length using page size of 8.5” x 11”.

Formatting templates:

• Word Doc File
• PDF example
• LaTeX templates

Submission webpage

Please submit your papers through the conference submission system here.

Review Process

Each submission will be peer reviewed by at least 2 peers.

Please note that the authors of each submitted paper will be expected to review one other paper.

Important Dates (All dates now firm)

Oct 28, 2022	Due date for full workshop papers submission
Nov 4, 2022	Notification of paper acceptance to authors
Nov 20,2022	Camera-ready of accepted papers
Dec 17-20 2022	Workshop (one day of)

Workshop Program Co-Chairs

Dr Stephen McGough
Reader in Machine Learning
School of Computing Science
Newcastle University
United Kingdom
E-mail : stephen.mcgough@newcastle.ac.uk

Dr Matthew Forshaw
Reader in Data Science
School of Computing
Newcastle University
United Kingdom
E-mail: matthew.forshaw@newcastle.ac.uk

Prof Gavin Shaddick
Chair of Data Science & Statistics
College of Engineering, Mathematics and Physical Sciences
University of Exeter
Exeter, UK, EX4 4QF
United Kingdom
Email: G.Shaddick@exeter.ac.uk

Dr Hao Dong
Assistant Professor in AI
Peng Cheng Laboratory
Peking University
Haidian District, Beijing
China
Email: hao.dong@pku.edu.cn

International Technical Committee

To be confirmed

Rabih Bashroush	University of East London, UK
Raffaele Bruno	Institute for informatics and telematics National Research Council, Pisa, Italy
Dongrui Fan	Chinese Academy of Science, Beijing, China
Amlan Ganguly	Rochester Institute of Technology, Rochester, New Yorlk, USA
Rong Ge	Clemson University, Clemson, South Carolina, USA
Rameshwar Dubey	Montpellier Business School, France