#Call for Papers: The 10th International Conference on Big Data and Information Analytics (BigDIA 2024)

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. Accepted papers will be submitted for inclusion into the IEEE XploreTM.

Social issues of interest include but not limited to:

1. Healthcare: Disease Surveillance and Management, Healthcare Access, Humanitarian Health Care, etc.
2. Education: Personalized Learning, Performance Analytics, etc.
3. Urban Planning and Development: Smart Cities, Access to Housing, etc.
4. Environmental Sustainability: Climate Change Mitigation, Natural Resource Management, Green Energy, etc.
5. Public Safety and Crime Prevention: Predictive Policing, Disaster Response, Emergency Management, etc.
6. Social Inequality: Income and Wealth Distribution, Access to Services, etc.
7. Food Security: Agricultural Productivity, Supply Chain Management, etc.
8. Gender Equality: Monitoring Discrimination, Empowerment Programs, etc.

Technical Approaches:

1. Big Data Analytics
2. Social Network Analysis
3. Machine Learning
4. Game Theory
5. Intelligent Decision Making
6. Empirical Research Method
7. Explainable Artificial Intelligence
8. Social Impact Assessment
9. Multi Agents and Robots Systems
10. Embodied Intelligence

Three primary selection criteria for all submissions are:

1. 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.
2. Relevance and Social Impact: Assess the alignment of the work with the SDGs and LNOB principles, and its potential social impact.
3. 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(https://www.ieee.org/conferences/publishing/templates.html), 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(https://easychair.org/cfp/BigDIA2024).

#Important Dates

Paper Submission Deadline:August 10,2024
Notification of Acceptance:September 7,2024
Camera-ready Submission:September 30,2024
BigDIA 2024 Conference:October 25-28,2024

#Past Conferences (The papers from all previous conferences are retrievable in the EI database)
The 9th International Conference on Big Data and Information Analytics
BigDIA 2023, Haikou, China–December 15-17, 2023
https://2023.bigdia.org/

The 8th International Conference on Big Data and Information Analytics (BigDIA 2022)
BigDIA 2022, Guiyang, China–August 24-25, 2022
https://BigDIA2022.scimeeting.cn

The 7th International Conference on Big Data and Information Analytics (BigDIA 2021)
BigDIA 2021, Chongqing, China–October 29-21, 2021
http://bigdia2021.cqupt.edu.cn/