会议信息

BDCAT 2026: IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

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BDCAT
截稿日期:
2026-08-19 还有 16 天
通知日期:
2026-09-30
会议日期:
2026-12-01
会议地点:
Florianopolis, Brazil
届数:
ICORE: C   浏览: 24820   关注: 13   参加: 4

征稿

BDCAT 2026 (IEEE/ACM International Conference on Big Data Computing, Applications and Technologies) is a ICORE C conference held in Florianopolis, Brazil on 2026-12-01. The paper submission deadline is 2026-08-19. Acceptance notifications are sent on 2026-09-30.

Conference Overview 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 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. The IEEE/ACM BDCAT 2026 will be held in conjunction with the 19th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2026) in Florianopolis, Brazil. Call for Papers Details Authors are invited to submit original, unpublished research manuscripts in all areas of Big Data computing, applications, and technologies, as well as on related scaling data analysis.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): 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 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
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