Call for Papers: Big Data and Decision Analytics for Social Good(Special Track: D24SG)

Big data and decision analytics are opening new avenues to address challenges in the field of social welfare, with its importance and urgency underscored by the United Nations Sustainable Development Goals (SDGs) and the Leave No One Behind (LNOB) principle. With a strong focus on emerging areas such as big data, artificial intelligence, and intelligent decision, this special track aims to derive actionable insights and develop innovative data-driven solutions that aid decision-making in government, non-profit organizations, and businesses within the social good sector, thereby promoting sustainable development goals. We encourage multidisciplinary research across computer, social, and natural sciences, and advocate close collaboration with research communities, NGOs, and government agencies to strengthen the connection between data science, policy making, and social welfare.

Accepted research papers in the Big Data and Decision Analytics for Social Good track will be included in the BigDIA proceedings. An award will be given to honour outstanding research papers in this track.

Social issues of interest include but not limited to:

  • Healthcare: Disease Surveillance and Management, Healthcare Access, Humanitarian Health Care, etc.

  • Education: Personalized Learning, Performance Analytics, etc.

  • Urban Planning and Development: Smart Cities, Access to Housing, etc.

  • Environmental Sustainability: Climate Change Mitigation, Natural Resource Management, Green Energy, etc.

  • Public Safety and Crime Prevention: Predictive Policing, Disaster Response, Emergency Management, etc.

  • Social Inequality: Income and Wealth Distribution, Access to Services, etc.

  • Food Security: Agricultural Productivity, Supply Chain Management, etc.

  • Gender Equality: Monitoring Discrimination, Empowerment Programs, etc.

Technical Approaches:

  • Big Data Analytics
  • Social Network Analysis
  • Machine Learning
  • Game Theory
  • Intelligent Decision Making
  • Empirical Research Method
  • Explainable Artificial Intelligence
  • Social Impact Assessment
  • Multi Agents and Robots Systems
  • Embodied Intelligence

Three primary selection criteria for all submissions are:

  • Scientific Quality and Innovation: Evaluate the work’s advancement and unique contribution to the fields of big data analytics, decision science, and social or natural sciences.

  • Relevance and Social Impact: Assess the alignment of the work with the SDGs and LNOB principles, and its potential social impact.

  • Collaboration and Practical Outcomes: Examine the depth of collaboration with industry experts and civil society stakeholders, and the effectiveness in applying and interpreting real-world data.

Paper Submission

Papers must be submitted anonymously, written in English, follow the IEEE conference template format which can be downloaded at here, and not exceed 8 pages (including references). Accepted papers will have the opportunity to be included in the IEEE Xplore™ database. Please submit your paper in PDF format through the EasyChair platform.

Important Dates

2024

August 10 August 25

Paper Submission Deadline

September 7 September 10

Notification of Acceptance

September 30

Camera-ready Submission

October 25-28

BigDIA 2024 Conference

Organization

Special Track General Chairs:

  • Mounir Ghogho, The International University of Rabat, Rabat, Morocco.
  • Kaiming Xiao, Laboratory for Big Data and Decision, Changsha, China.
  • Lihua Liu, Laboratory for Big Data and Decision, Changsha, China.

Special Track Co-Chairs:

  • Senzhang Wang, Central South University, Changsha, China.
  • Qiuju Chen, University of Science and Technology of China, Hefei, China.
  • Yun Zhou, National Key Laboratory of Information Systems Engineering, Changsha, China.
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Important Dates

2024

August 10 August 25

Paper Submission Deadline

September 1 September 10

Notification of Acceptance

September 30

Camera-ready Submission

October 25-28

BigDIA 2024 Conference