Call for Papers, IEEE Big Data 2019

2019 IEEE International Conference on Big Data (IEEE BigData 2019)
http://cci.drexel.edu/bigdata/bigdata2019/index.html
December 10-13, 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://cci.drexel.edu/bigdata/bigdata2013/) and the regular paper acceptance rate is 17.0%.
  • The IEEE Big Data 2017 ( http://cci.drexel.edu/bigdata/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://cci.drexel.edu/bigdata/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 Seattle this year. Example topics of interest includes but is not limited to the following:

  1. Big Data Science and Foundations
    1. Novel Theoretical Models for Big Data
    2. New Computational Models for Big Data
    3. Data and Information Quality for Big Data
    4. New Data Standards
  2. Big Data Infrastructure
    1. Cloud/Grid/Stream Computing for Big Data
    2. High Performance/Parallel Computing Platforms for Big Data
    3. Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment
    4. Energy-efficient Computing for Big Data
    5. Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data
    6. Software Techniques and Architectures in Cloud/Grid/Stream Computing
    7. Big Data Open Platforms
    8. New Programming Models for Big Data beyond Hadoop/MapReduce, STORM
    9. Software Systems to Support Big Data Computing
  3. Big Data Management
    1. Search and Mining of a variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
    2. Algorithms and Systems for Big DataSearch
    3. Distributed, and Peer-to-peer Search
    4. Big Data Search Architectures, Scalability and Efficiency
    5. Data Acquisition, Integration, Cleaning, and Best Practices
    6. Visualization Analytics for Big Data
    7. Computational Modeling and Data Integration
    8. Large-scale Recommendation Systems and Social Media Systems
    9. Cloud/Grid/Stream Data Mining- Big Velocity Data
    10. Link and Graph Mining
    11. Semantic-based Data Mining and Data Pre-processing
    12. Mobility and Big Data
    13. Multimedia and Multi-structured Data- Big Variety Data
  4.  Big Data Search and Mining
    1. Social Web Search and Mining
    2. Web Search
    3. Algorithms and Systems for Big Data Search
    4. Distributed, and Peer-to-peer Search
    5. Big Data Search Architectures, Scalability and Efficiency
    6. Data Acquisition, Integration, Cleaning, and Best Practices
    7. Visualization Analytics for Big Data
    8. Computational Modeling and Data Integration
    9. Large-scale Recommendation Systems and Social Media Systems
    10. Cloud/Grid/StreamData Mining- Big Velocity Data
    11. Link and Graph Mining
    12. Semantic-based Data Mining and Data Pre-processing
    13. Mobility and Big Data
    14. Multimedia and Multi-structured Data- Big Variety Data
  5. Ethics, Privacy and Trust in Big Data Systems
    1. Techniques and models for fairness and diversity
    2. Experimental studies of fairness, diversity, accountability, and transparency
    3. Techniques and models for transparency and interpretability
    4. Trade-offs between transparency and privacy
    5. Intrusion Detection for Gigabit Networks
    6. Anomaly and APT Detection in Very Large Scale Systems
    7. High Performance Cryptography
    8. Visualizing Large Scale Security Data
    9. Threat Detection using Big Data Analytics
    10. Privacy Preserving Big Data Collection/Analytics
    11. HCI Challenges for Big Data Security & Privacy
    12. Trust management in IoT and other Big Data Systems
  6. Hardware/OS Acceleration for Big Data
    1. FPGA/CGRA/GPU accelerators for Big Data applications
    2. Operating system support and runtimes for hardware accelerators
    3. Programming models and platforms for accelerators
    4. Domain-specific and heterogeneous architectures
    5. Novel system organizations and designs
    6. Computation in memory/storage/network
    7. Persistent, non-volatile and emerging memory for Big Data
    8. Operating system support for high-performance network architectures
  7. Big Data Applications
    1. Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication
    2. Big Data Analytics in Small Business Enterprises (SMEs),
    3. Big Data Analytics in Government, Public Sector and Society in General
    4. Real-life Case Studies of Value Creation through Big Data Analytics
    5. Big Data as a Service
    6. Big Data Industry Standards
    7. Experiences with Big Data Project Deployments

Industrial Track:
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).

Student Travel Award:
IEEE Big Data 2019 will offer student travel to student authors (including post-docs)

Paper Submission:
Please submit a full-length paper (up to 10 page IEEE 2-column format) through the online submission system.
https://wi-lab.com/cyberchair/2019/bigdata19/scripts/submit.php?subarea=BigD
Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (see link to “formatting instructions” below).

Formatting Instructions:
8.5″ x 11″ (DOC, PDF)
LaTex Formatting Macros

Important Dates:
Electronic submission of full papers: August 19, 2019
Notification of paper acceptance: Oct 16, 2019
Camera-ready of accepted papers: Nov 10, 2019
Conference: Dec 9-12, 2019

Conference Co-Chairs:
Dr. Roger Barga, Amazon.com, USA
Prof Carlo Zaniolo, UCLA, USA

Program Co-Chairs:
Dr. Chaitanya Baru, San Diego Supercomputer Center/UC 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. Zhou Yongluan, 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,