IEEE BigData

2026 IEEE International Conference on Big Data

Call for Special Session on Machine Learning on Big Data

11th IEEE Special Session Machine Learning on Big Data in IEEE BigData 2026

This follows the great success of ten previous editions co-located with the IEEE BigData and IEEE ICMLA conference series and focuses on machine learning models, techniques, and algorithms related to Big Data, a vibrant and challenging research context playing a leading role in the Machine Learning and Data Mining research communities.

Big data is gaining attention from researchers, being driven among others by technological innovations (such as cloud interfaces) and novel paradigms (such as social networks). Devising and developing machine learning models, techniques, and algorithms for big data represents a fundamental problem stirred up by the tremendous range of critical applications incorporating machine learning tools in their core platforms. For example, in application settings where big data arise and the machine is useful, we recognize, among other things:

  • Genome data management and mining can gain important benefits from machine learning algorithms.
  • Medical and e-health information systems usually include successful machine learning tools for processing and mining very large graphs modelling patient-to-disease, patient-to-doctor, and patient-to-therapy networks;
  • Machine-learning-based processing (e.g., acquisition, knowledge discovery, and so forth) over large-scale sensor networks introduces important advantages over classical data-management-based approaches; similarly,

Some trending topics in machine learning on big data include:

  • novel applications of machine learning on big data (e.g., healthcare, cybersecurity, smart cities, and so forth).
  • heterogeneous machine learning on big data;
  • temporal analysis and spatial analysis on big data;
  • privacy-preserving machine learning on big data;
  • machine-learning-based big data analytics;
  • machine-learning-based predictive approaches;
  • deep learning – models, principles, issues;
  • scalable machine learning algorithms;
  • machine learning over massive big data in distributed settings;
  • machine learning on unconventional big data sources (e.g., large-scale graphs in scientific applications, strongly-unstructured social networks, and so forth);

The MLBD 2026 special session focuses on all the research aspects of machine learning on Big Data. Among these, an unrestricted list includes:

  • Case Studies and Applications
  • Advanced Topics (e.g., LLM, Dimensionality Reduction, Matrix Approximation Algorithms, Multi-Task Learning, Semi-Supervised Learning, Integration with NoSQL Databases)
  • Architectures
  • Systems
  • Non-Conventional Big Data Settings (e.g., Spatio-Temporal Big Data, Streaming Big Data, Graph Big Data, Cloud Big Data, Probabilistic Big Data, Uncertain Big Data)
  • Innovative Architectures (GPU, Clouds, Clusters)
  • Algorithms
  • Innovative Techniques
  • Novel Paradigms
  • Statistical Approaches
  • Modelling
  • Fundamentals

The 11th IEEE Special Session "Machine Learning on Big Data" (MLBD 2026) of the 2026 IEEE International Conference on Big Data will be held in Phoenix, AZ, USA, from December 14-17, 2026, and it aims to synergistically connect the research community and industry practitioners. It provides an international forum where scientific domain experts and Machine Learning and Data Mining researchers, practitioners, and developers can share their findings in theoretical foundations, current methodologies, and practical experiences on Machine Learning on Big Data. MLBD 2026 will provide a stimulating environment to encourage discussion, fellowship, and exchange of ideas in all aspects of research related to Machine Learning on Big Data.

This includes both original research contributions and insights from practical system design, implementation, and evaluation, along with new research directions and emerging application domains in the target area. An expected outcome from MLBD 2026 is the identification of new problems in the main topics, and moves to achieve consolidated solutions to already-known problems. Other goals are to help in creating a focused community of scientists who create and drive interest in the area of Machine Learning on Big Data, and additionally to continue on the success of the event across future years.

Special Session Location : Phoenix, AZ, USA

Submission Guidelines and Instructions:

Maximum paper length allowed is:

  • Position Papers: 6 pages.
  • Demo Papers: 6 pages;
  • Short Papers: 6 pages;
  • Full Papers: 10 pages;

Submitted papers will be thoroughly reviewed by members of the Special Session Program Committee for quality, correctness, originality, and relevance. All accepted papers must be presented by one of the authors, who must register. Papers must be submitted via the CyberChair System by selecting the track "SpecialSession on Machine Learning on Big Data".

Paper Publication:

Accepted papers will appear in the official IEEE Big Data 2026 main conference proceedings, published by IEEE.

Important Dates:

  • Special Session: December 14-17, 2026
  • Camera-ready paper due: November 14, 2026
  • Notification of acceptance: October 31, 2026
  • Paper submission: September 30, 2026

Program Committee:

  • Apostolos Papadopoulos, Aristotle University of Thessaloniki, Greece
  • Enzo Mumolo, University of Trieste, Italy
  • Sofian Maabout, LABRI, Bordeaux University, France
  • Carson K. Leung, University of Manitoba, Canada
  • Rocco Langone, Deloitte, Belgium
  • Mark Last, Ben-Gurion University of the Negev, Israel
  • Marwan Hassani, TU Eindhoven, The Netherlands
  • Joao Gama, University of Porto, Portugal
  • Alfredo Cuzzocrea, University of Calabria, Italy
  • Michelangelo Ceci, University of Bari, Italy
  • Alfredo Cuzzocrea, University of Calabria, Italy (Program Committee Chair)