The DSAA'2026 Application, Data and Benchmark Track solicits high-quality, original papers presenting applications and best practices of Data Science and Advanced Analytics across various disciplines and domains, including business, government, health and medical science, physical sciences, arts, humanity, and social sciences.
Submissions are expected to address problems on real-life data and the results can ideally be reproducible through a public git repository. Furthermore, submitted papers are expected to provide interesting, insightful results to policy-makers, end-users, or practitioners of Data Science and Advanced Analytics or to highlight new challenges for researchers motivated by the specific needs and characteristics of application areas.
Topics of interests include but are not limited to:
- Generative AI applications
- Domain-specific data science and analytics practice, including customer analytics, business analytics, financial analytics, risk analytics, operational analytics, and management analytics
- Data science for health, care, medicine, biomedical science, humanity, and human science
- Data science for scientific domains, such as physics, astronomy, chemistry, biology and material science
- Data science for engineering such as electrical, mechanical, manufacturing, mining, and environmental engineering
- Government analytics and enterprise analytics
- Data science for social and public good
- Cloud, crowd, online, mobile, decentralized, edge and distributed data analytics
- Business, economic, environmental, social and sustainable impact modeling
- Impactful real-world applications, case studies and demonstrations
- Operationalizable infrastructures, platforms, and tools
- Deployment, management and policy-making
- Ethics, social issues, privacy, trust, fairness and bias
- Reflections and lessons for better data/analytics practices
Submissions for the DSAA'2026 Application, Data and Benchmark Track should very clearly specify the problem being solved, what methodologies were used to solve the problem, what data was used, how the results were evaluated, and how the solution is being used (ideally in production). Applying new data science and analytics methods to public data or data downloaded from competition sites (such as Kaggle), without a real problem (and problem owner) will not be accepted in this track.
Paper Submissions
All papers should be submitted electronically via Open Review (under the Research Track).
The length of each paper submitted to the Research tracks should be no more than seven (7) pages of technical content plus additional pages solely for references and should be formatted following the standard 2-column U.S. letter style of the IEEE Conference template. For further information and instructions, see the IEEE Proceedings Author Guidelines.
All submissions will be blind reviewed by the Program Committee on the basis of:
- Technical quality
- Relevance to the conference's topics of interest
- Originality
- Significance
- Clarity
Author names and affiliations must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Submissions failing to comply with formatting or anonymity will be rejected without review.
Because of the double-blind review process, non-anonymous papers that have been issued as technical reports or similar cannot be considered for DSAA'2026. An exception applies to arXiv papers that were published in arXiv at least one month prior to the DSAA'2026 submission deadline, provided that the submitted paper's title and abstract differ from the arXiv version.
Enquiries
General enquiries about Application, Data and Benchmark Track paper submissions should be directed to Track Chairs.
Application, Data and Benchmark Chairs
- Partha Pratim Roy, Indian Institute of Technology, India
- Ladjel Bellatreche, National Engineering School for Mechanics and Aerotechnics, France