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Big Data Analytics for Sustainability (BDA4S) 2023

Branching

A Workshop at 2023 IEEE International Conference on Big Data (IEEE Big Data 2023)

December 15th - 18th, 2023, Sorrento, Italy

## Final Programme

Time Title Authors
14:00 - 14:05 Welcome Chairs
14:05 - 14:25 Explainable Deep Learning for Smart Grid Stability Detection Giovanni Ciaramella, Fabio Martinelli, Francesco Mercaldo, and Antonella Santone
14:25 - 14:45 The Forecastability of Underlying Building Electricity Demand from Time Series Data Mohamad Khalil, A. Stephen McGough, Hussain Kazmi, and Sara Walker
14:45 - 15:05 Forecasting Resource Demand for Dynamic Datacenter Sizing in Telco Infrastructures Dimitra Paranou, Angelos Pentelas, Dimitris Katsiros, Konstantinos Maidatsis, George Giannopoulos, Evangelos Angelou, Nikos Anastopoulos, and George Papastefanatos
15:05 - 15:25 Natural Language Processing to Understand Human Activities Impacted by Hydroelectric Energy Projects Keshava Pallavi Gone, Yan Chen, and Michael Smit
15:25 - 15:45 Wireless Sensor Networks Calibration using Attention-based Gated Recurrent Units for Air Pollution Monitoring Shagufta Henna, Asif Yar, and Kazeem Saheed
15:45 - 16:05 A Methodology and a Toolbox to Explore Dataset related to the Environmental Impact of HTTP Requests Christophe Cerin, Mathilde Jay, Laurent Lefevre, and Denis Trystram
16:05 - 16:30 Coffee Break  
16:30 - 16:50 Detection of methane plumes using Sentinel-2 satellite images and deep neural networks trained on synthetically created label data Maciel Zortea, Joao Lucas de Sousa Almeida, Levente Klein, and Alberto Costa Nogueira Junior
16:50 - 17:10 Energy-Aware Query Processing: A Case Study on Join Reordering Ladjel Bellatreche, Fouad Djellali, Wojciech Macyna, and Carlos Ordonez
17:10 - 17:30 Is Active Learning Green? An Empirical Study Shirin Salehi and Anke Schmeink
17:30 - 17:50 Explainable Long-Term Forecasting of Air Pollution Abdul Mutakabbir and Samuel A. Ajila
17:50 - 18:10 Counterfactual Analysis: A Case Study on Impact of External Events on Building Energy Consumption Carolina Minami Oguchi, Dipak Ghosal, Alex Sim, and Kesheng Wu
18:10 - 18:20 Closing Remarks  

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

Computing for Sustainability

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:

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)

please contact us Due date for full workshop papers submission
Nov 1, 2023 Notification of paper acceptance to authors
Nov 20,2023 Camera-ready of accepted papers
Dec 15-18 2023 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