Note: We have received an NSF award to provide travel support for student authors. More details will be announced.

The 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2017) aims 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. The first 3 events were held in London (BDC 2014), Cyprus (BDC 2015), and Shanghai (BDCAT 2016). BDCAT 2017 will be held in conjunction with the 10th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2017) in Austin, Texas, USA.

Topics of interest include, but are not limited to:

  1. Big Data Science
  • Big Data Analytics
  • Data Science Models and Approaches
  • Algorithms for Big Data
  • Big Data Search and Information Retrieval Techniques
  • Data Mining and Knowledge Discovery Approaches
  • Machine Learning Techniques for Big Data
  • Big Data Acquisition, Integration, Cleaning, and Best Practices
  • Big Data and Deep Learning in Physics, Information, HPC, HEP, Astrophysics and related areas

2. Big Data Infrastructures and Platforms

  • Scalable Computing Models, Theories, and Algorithms
  • In-Memory Systems and Platforms for Big Data Analytics
  • Cyber-Infrastructure for Big Data
  • Performance Evaluation Reports for Big Data Systems
  • Storage Systems (including file systems, NoSQL, and RDBMS)
  • Resource Management Approaches for Big Data Systems
  • Many-Core Computing and Accelerators

3. Big Data Applications

  • Big Data Applications for Internet of Things
  • Mobile Applications of Big Data
  • Big Data Applications for Smart City
  • Healthcare Applications such as Genome Processing and Analytics
  • Scientific Application Case Studies on Cloud Infrastructure
  • Big Data in Social Networks
  • Data Streaming Applications

4. Big Data Trends and Challenges

  • Fault Tolerance and Reliability
  • Scalability of Big Data Systems
  • Energy-Efficient Algorithms
  • Big Data Privacy and Security
  • Big Data Archival and Preservation

5. Visualization of Big Data

  • Visual Analytics Algorithms and Foundations
  • Graph and Context Models for Visualization
  • Analytics Reasoning and Sense-making on Big Data
  • Visual Representation and Interaction
  • Big Data Transformation, and Presentation

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