In recent years, there has been significant interest in the use of Machine Learning and AI-based techniques to support large 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 realise 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 IEEE/ACM International Conference on Big Data Computing, Applications, and Technologies (BDCAT) is a premier annual conference series aiming to provide a platform for researchers from both academia and industry to present 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) and Vancouver, USA (BDCAT 2022). BDCAT 2023 will be held in conjunction with the 16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2023) in Taormina, Messina, Italy.

Authors are invited to submit original, unpublished research manuscripts in all areas of Big Data computing, applications and technologies, as well as on related scaling data analysis.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

    Scaling Machine Learning and Data Mining
        Data Science Models and Approaches
        Data Acquisition, Integration, Cleaning and Best Practices
        Supervised, Unsupervised and Reinforcement Learning
        Neural Networks, Convolution Neural Networks and Recurrent Neural Networks
        Transformer and Natural Language Processing
        Swarm Intelligence and Evolutionary Strategy
        Efficient Model Training, Inference and Serving
        Distributed, Federated and Parallel Learning Algorithms
        Testing, Debugging and Monitoring
        Fairness, Interpretability and Explainability
        Specialized Hardware for Scaling
    Scaling Data Infrastructures and Platforms
        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
    Scaling Data Applications
        Data Applications for Internet of Things, Mobile Applications and Cyber-Physical Systems
        Data Applications for Healthcare and Life Science (e.g., Genome Processing)
        Data Applications for Physical Science and Engineering
        Data Applications for Business and Enterprise Applications
        Data Applications for Social Networks
        Data Applications for Scientific Case Studies
        Data Applications over the Cloud-Edge Continuum
        Data Streaming and Batch Applications
        Data Trends and Challenges
    Scaling Data Visualization
        Visual Analytics Algorithms and Foundations
        Graph and Context Models for Visualization
        Analytics Reasoning and Sense-making
        Visual Representation and Interaction