We are pleased to announce that the “12th ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2025)” will be held in Nantes, France between 1st and 4th December, 2025.

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). On the other hand, 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 ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) is a premier annual international conference series aiming to provide 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. Previous events were held in London, UK (BDCAT 2014), Limassol, Cyprus (BDCAT 2015), Shanghai, China (BDCAT 2016), Austin, USA (BDCAT 2017), Zurich, Switzerland (BDCAT 2018), Auckland, New Zealand (BDCAT 2019), Leicester, UK (BDCAT 2020), Leicester, UK (BDCAT 2021), Vancouver, USA (BDCAT 2022), Taormina, Italy (BDCAT 2023), Sharjah, UAE (BDCAT 2024). The BDCAT 2025 will be held in conjunction with the 18th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2025) in Nantes, France.

Authors are invited to submit original, unpublished research manuscripts in all areas of Big Data computing, applications and technologies, as well as on related scaling data analysis.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 not limited to):

    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, Transformer, Large Language Model
        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
    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
    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
    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