Call for Special Session: Information Granulation in Data Science and Scalable Computing
The 12th Special Session on Information Granulation in Data Science and Scalable Computing
December 14-17, 2026 · Phoenix, Arizona, USA
Background
Granular Computing is a general computation approach for a usage of information granules such as data blocks, clusters, groups, as well as value intervals, sets, hierarchies, etc., to build efficient computational models for complex Big Data applications, characterized by huge amounts of diverse data and associated domain knowledge. Information Granulation, under different names, has appeared in many fields, such as granularity in artificial intelligence, divide and conquer methods for scaling calculations, approximate computing, knowledge representation, topological data analysis, image processing and many others related with human and artificial intelligence. The principles of Granular Computing can be also helpful to design simplified descriptions of complex data systems and to bridge the gap between the humans and AI. Herein we may follow the phrase “Information Granules = Fundamental Pieces of Human and Machine Knowledge” and treat Granular Computing as one of important meta-mathematical methodologies for Big Data Analytics.
Session Scope
The 12th session in this series continues to address the theory and practice of derivations and computations based on various types of granular models and structures. It provides researchers from both academia and industry with the means to present the state-of-the-art results and methodologies related to Information Granulation and Granular Computing, with a special emphasis on applications in Data Science and Scalable Computing. The session also refers – from the particular viewpoint of Information Granulation – to currently important research tracks such as social network computing, cloud computing, cyber-security, data mining, process mining, machine learning, statistics, knowledge management, AI-based systems, soft computing, e-Intelligence, business intelligence, bioinformatics, health informatics and IoT. The papers addressing Information Granulation in the emerging field of XAI and using its principles to construct interpretable AI models are highly welcome as well. Particularly, we encourage the papers which deliver experimental results but in the same time, provide theoretical foundations to justify those results.
Highlights
The session is organized as a part of the IEEE Big Data 2026 conference (December 14-17), which is a well-established and competitive international event targeted at modern trends in big data processing and analytics. The session is intended to be a forum for discussing ideas, issues and methods based on and inspired by Information Granulation and Granular Computing, in an atmosphere promoting free exchange of viewpoints and perspectives coming from different application areas.
Papers accepted to the session will be published in the IEEE Big Data 2026 conference proceedings, together with papers accepted to the main conference track. Organizers are planning a special issue in a relevant scientific journal, such as Big Data Research (Elsevier), Granular Computing (Springer), Review of Socionetwork Strategies (Springer), or Big Data Mining and Analytics (Tsinghua University Press). Organizers particularly encourage papers which deliver experimental results but in the same time, provide theoretical foundations to justify those results.
Organizers
- Shusaku Tsumoto, Shimane University, Japan, tsumoto@med.shimane-u.ac.jp
- Dominik Slezak, QED Software & University of Warsaw, Poland, dominik.slezak@qedsoftware.com
- Tzung-Pei Hong, National University of Kaohsiung, Taiwan, tphong@nuk.edu.tw
- Weiping Ding, Nantong University, China, dwp9988@hotmail.com
Important Dates
- Full paper submission: August 21, 2026
- Notification of paper acceptance: October 24, 2026
- Camera-ready of accepted papers: November 14, 2026
- Conference: December 14-17, 2026