[hpc-announce] IEEE BDCAT 2026 Call For Papers

[Flavia Delicato]

IEEE BDCAT 2026 Call For Papers

Florianópolis, Brazil

December 1-4, 2026

https://urldefense.us/v3/__https://bdcat-conference.org/__;!!G_uCfscf7eWS!ZpSdQma8Rb-wUJUNMhhtu3cvploTZlE0MlwuhdrjqJxA92Bq3LDTGcJutoECEZapY_YsQylGTl9mHzIbNt-GLcnw$ 



We are pleased to announce that the *13th IEEE International Conference on
Big Data Computing, Applications and Technologies (BDCAT 2026)* will be
held in Florianópolis, Brazil, between 1st and 4th December 2026.

Recent years have witnessed significant interest in the use of Machine
Learning and AI-based techniques to support large-scale data analysis, with
research and implementation of systems specifically focused on supporting
different phases of the data processing lifecycle. These have ranged from
in-memory systems and distributed environments (e.g., MapReduce/Hadoop,
Spark) to specialist environments for stream processing of data and events
(e.g., Flink, Kinesis) and Serverless (e.g., OpenWhisk, AWS Lambda). We
also recognize the importance of computational systems required to process
small data volumes, but which involve interdependencies and relationships
that are hard to capture and derive.

The International Conference on Big Data Computing, Applications and
Technologies (BDCAT) is a premier annual international conference series
that provides a forum for researchers from both academia and industry to
present and discuss new discoveries in the broad area of big data computing
and applications. The IEEE *BDCAT 2026* will be held in conjunction with
the *19th IEEE International Conference on Utility and Cloud Computing (UCC
2026)* in Florianopolis, Brazil.

Authors are invited to submit original, unpublished research manuscripts in
all areas of Big Data computing, applications, and technologies, as well as
on related scaling data analysis.

*Topics of interest include (but are not limited to):*

   1. *Machine Learning and Data Mining*

○       Data Science Models and Approaches

○       Supervised, Unsupervised, Semi-supervised*,* and Reinforcement
Learning

○       Neural Networks, Convolution Neural Networks, and Recurrent Neural
Networks

○       Autoencoders, Transformers, Large Language Models

○       Natural Language Understanding, Natural Language Processing

○       Swarm Intelligence and Evolutionary Strategy

○       Computational Efficient Model Training, Inference, and Serving

○       Distributed, Federated, and Parallel Learning Algorithms

○       Fairness, Interpretability, and Explainability

   2. *Data Processing and Infrastructures/Platforms*

○       Data Acquisition, Integration, Cleaning, and Best Practices

○       Scalable Computing Models, Theories, and Algorithms

○       MapReduce: Hadoop and Spark

○       Privacy and Security over the Data Life Cycle

○       Data Search and Information Retrieval Techniques

○       Extract/Transform/Load (ETL) or ETL Pipelines

○       In-Memory Systems and Platforms

○       Performance Evaluation Reports

○       Storage Systems (including file systems, NoSQL, and RDBMS)

○       Resource Management Approaches

○       Data Analytics on Edge Devices

○       Fault Tolerance and Reliability

○       Energy-Efficiency and Sustainability

○       Data Archival and Preservation

○       Testing, Debugging, and Monitoring

○       Specialized Hardware for Scaling

   3. *Applications Domains*

○       Internet of Things, Mobile Applications, and Cyber-Physical Systems

○       Healthcare and Life Science (e.g., Genome Processing)

○       Physical Science and Engineering

○       Business and Enterprise Applications

○       Social Network Analysis

○       Scientific Case Studies and Workflows

○       Risk Analysis and Management

○       Cloud-Edge Continuum

○       Data Streaming and Batch Applications

○       Data Trends and Challenges

   4. *Data Visualization and Analytics*

○       Visual Analytics Algorithms and Foundations

○       Graph and Context Models for Visualization

○       Analytics Reasoning and Sense-making

○       Visual Representation and Interaction

○       Data Transformation and Presentation
Paper Submission

Submitted manuscripts must represent original and unpublished research that
is not currently under review for any other conference or journal. Full
papers should be submitted in PDF format, using the IEEE conference format
with double-column pages including figures, tables, and references. Short
papers should not exceed four (4) pages, and poster submissions should have
at most two (2) pages in length. All manuscripts undergo a peer review
process and will be reviewed and judged on correctness, originality,
technical strength, rigor in analysis, quality of results, quality of
presentation, and interest and relevance to the conference attendees.
Important Dates

Paper Submission Deadline: August 19, 2026

Acceptance Notification: September 30, 2026

Camera-Ready Papers Due: October 15, 2026

Conference Dates: December 1–4, 2026

*Additional notes:*

*If Generative AI tools are used,  these should be acknowledged in the
paper.*

At least one author of each paper must be registered for the conference and
present the paper in order for the paper to be published in the
proceedings. The conference proceedings will be published by the IEEE and
made available online via the IEEE Xplore Digital Library.



-- 
Flávia C. Delicato
Professora Titular - Full Professor
Instituto de Computação
Universidade Federal Fluminense - UFF
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