4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, Austin, Vereinigte Staaten

Abbrevation

IEEE/ACM BDCAT

Link

http://dsr.encs.vancouver.wsu.edu/BDCAT2017/

City

Austin

Country

United States

Deadline Paper

2017-07-15

Start Date

2017-12-05

End Date

2017-12-08

Abstract

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: 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