[hpc-announce] CFP- “11th IEEE/ACM International Conference on Big Data Computing, Applications, and Technologies (BDCAT 2024)”

[Rajkumar Buyya]

“11th IEEE/ACM International Conference on Big Data Computing, 
Applications, and Technologies (BDCAT 2024)”

Sharjah, United Arab Emirates, December 16-19, 2024.

Website:
https://urldefense.us/v3/__https://www.uccbdcat2024.org/bdcat/__;!!G_uCfscf7eWS!b71XaYp2e4eGLX6lccbYTDH3Edj-k-rPAdOOPOSEMaPN-B1_VQz0khEiMH28vVAhkyIXSWfjNUJ9ha9nING8c2cgUZo$ 

We are pleased to inform you that the “11th IEEE/ACM International 
Conference on Big Data Computing, Applications, and Technologies (BDCAT 
2024)” will be held in Sharjah, United Arab Emirates between 16 and and 
19 December, 2024.

Recent years witnessed 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), Vancouver, USA (BDCAT 2022), and Taormina, 
Italy (BDCAT 2023). The BDCAT 2024 will be held in conjunction with the 
17th IEEE/ACM International Conference on Utility and Cloud Computing 
(UCC 2024) in Sharjah, UAE.



Authors are invited to submit original, unpublished research manuscripts 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):

1. 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

2. 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

3. 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

4. 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

PAPER SUBMISSION
Submitted manuscripts must represent original unpublished research that 
is not currently under review for any other conference or journal. 
Manuscripts are submitted in PDF format and may not exceed ten (10) 
ACM-formatted *double-column* pages, including figures, tables, and 
references. All manuscripts undergo a double-blind 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. 
Your submission is subject to a determination that you are not under any 
sanctions by ACM. Accepted papers will later be converted into 
single-column format through the ACM TAPS process and therefore need to 
use the new templates that are single-column by default. Switch them to 
double-column for authoring your paper. This is possible in both the 
Word and the LaTeX templates.

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

IMPORTANT DATES
Time zone: Anywhere in the world!
Paper Submissions Due: 10 August 2024
Acceptance Notification: 7 October 2024
Camera Ready Papers Due: 20 October 2024
AWARDS AND SPECIAL ISSUES
A selection commission chaired by the BDCAT 2024 technical programme 
committee will select and acknowledge the best paper to receive an award 
during the conference.
Authors of highly rated papers from BDCAT 2024 will be invited to submit 
an extended version to special issues of prestigious journals.