2019 IEEE International Conference on Big Data (IEEE Big Data 2019)
December 9-12, 2019, Los Angeles, CA, USA
In recent years, “Big Data” has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately our society itself. The IEEE Big Data conference series started in 2013 has established itself as the top tier research conference in Big Data.
- The first conference IEEE Big Data 2013 had more than 400 registered participants from 40 countries ( http://bigdataieee.org/BigData2013/) and the regular paper acceptance rate is 17.0%.
- The IEEE Big Data 2017 ( http://bigdataieee.org/BigData2017/ , regular paper acceptance rate: 17.8%) was held in Boston, MA, Dec 11-14, 2017 with close to 1000 registered participants from 50 countries.
- The IEEE Big Data 2018 ( http://bigdataieee.org/BigData2018/ , regular paper acceptance rate: 19.7%) was held in Seattle, WA, Dec 10-13, 2018 with close to 1100 registered participants from 47 countries.
The 2019 IEEE International Conference on Big Data (IEEE BigData 2019) will continue the success of the previous IEEE Big Data conferences. It will provide a leading forum for disseminating the latest results in Big Data Research, Development, and Applications.
We solicit high-quality original research papers (and significant work-in-progress papers) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity), including the Big Data challenges in scientific and engineering, social, sensor/IoT/IoE, and multimedia (audio, video, image, etc.) big data systems and applications. The conference adopts single-blind review policy. We expect to have a very high quality and exciting technical program at Los Angeles this year.
Example topics of interest includes but is not limited to the following:
1. Big Data Science and Foundations
2. Big Data Infrastructure
3. Big Data Management
4. Big Data Search and Mining
5. Ethics, Privacy and Trust in Big Data Systems
6. Hardware/OS Acceleration for Big Data
7. Big Data Applications
The Industrial Track solicits papers describing implementations of Big Data solutions relevant to industrial settings. The focus of industry track is on papers that address the practical, applied, or pragmatic or new research challenge issues related to the use of Big Data in industry. We accept full papers (up to 10 pages) and extended abstracts (2-4 pages).
IEEE Big Data 2019 will offer student travel to student authors (including post-docs)
Please submit a full-length paper (up to 10 page IEEE 2-column format)
through the online submission system.
Paper Submission Page
Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (see link to "formatting instructions" below).
8.5" x 11" (DOC, PDF)
LaTex Formatting Macros
Conference Co-ChairsDr. Roger Barga : Amazon.com, USA
Prof Carlo Zaniolo : UCLA, USA
Program Co-ChairsDr. Chaitanya Baru : San Diego Supercomputer Center/Univ. of California San Diego, USA
Dr. Jun (Luke) Huan : Baidu Big Data Lab, China
Prof. Latifur Khan : University of Texas at Dallas, USA
Vice Chairs in Big Data Science and Foundations
Prof. Jingrui He : UIUC, USA
Prof. Wenqing Hu : Missouri S&T University, USA
Vice Chairs in Big Data Infrastructure
Prof. Hanghang Tong : UIUC, USA
Dr. Yinglong Xia : Huawei, USA
Vice Chairs in Big Data Management
Prof. Christopher Jermaine : Rice University, USA
Prof. Yongluan Zhou : Univ. of Copenhagen, Denmark
Vice Chairs in Big Data Search and Mining
Prof. Quanquan Gu : UCLA, USA
Prof. Aditya Prakash : Virginia Tech, USA
Vice Chairs in Big Data Security, Privacy and Trust
Prof. Dongwon Lee : Penn State University, USA
Prof. Julia Stoyanovich : New York University, USA
Vice Chairs in Hardware/OS Accelerating for Big Data
Prof. Sang-Woo Jun, UC Irvine, USA
Prof. Harry Xu, UCLA, USA
Vice Chairs in Big Data Applications
Prof. Xia Ning : Ohio State University, USA
Prof. Tim Weninger : Univ. of Notre Dame, USA
Industry and Government Program Committee Co-Chairs
Dr. Ronay Ak : NVIDIA, USA
Dr. Yuanyuan Tian : IBM Almaden Research Center, USA