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Authors are invited to submit original, unpublished research manuscripts in all areas of Big Data computing, applications and technologies, as well as on related scaling data analysis.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