Call for Papers



2019 IEEE International Conference on Big Data (IEEE BigData 2019)

http://bigdataieee.org/BigData2019/

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://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 Seattle this year. Example topics of interest includes but is not limited to the following:



1. Big Data Science and Foundations

a. Novel Theoretical Models for Big Data

b. New Computational Models for Big Data

c. Data and Information Quality for Big Data

d. New Data Standards



2. Big Data Infrastructure

a. Cloud/Grid/Stream Computing for Big Data

b. High Performance/Parallel Computing Platforms for Big Data

c. Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment

d. Energy-efficient Computing for Big Data

e. Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data

f. Software Techniques and Architectures in Cloud/Grid/Stream Computing

g. Big Data Open Platforms

h. New Programming Models for Big Data beyond Hadoop/MapReduce, STORM

i. Software Systems to Support Big Data Computing



3. Big Data Management

a. Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data

b. Algorithms and Systems for Big DataSearch

c. Distributed, and Peer-to-peer Search

d. Big Data Search Architectures, Scalability and Efficiency

e. Data Acquisition, Integration, Cleaning, and Best Practices

f. Visualization Analytics for Big Data

g. Computational Modeling and Data Integration

h. Large-scale Recommendation Systems and Social Media Systems

i. Cloud/Grid/Stream Data Mining- Big Velocity Data

j. Link and Graph Mining

k. Semantic-based Data Mining and Data Pre-processing

l. Mobility and Big Data

m. Multimedia and Multi-structured Data- Big Variety Data





4. Big Data Search and Mining

a. Social Web Search and Mining

b. Web Search

c. Algorithms and Systems for Big Data Search

d. Distributed, and Peer-to-peer Search

e. Big Data Search Architectures, Scalability and Efficiency

f. Data Acquisition, Integration, Cleaning, and Best Practices

g. Visualization Analytics for Big Data

h. Computational Modeling and Data Integration

i. Large-scale Recommendation Systems and Social Media Systems

j. Cloud/Grid/StreamData Mining- Big Velocity Data

k. Link and Graph Mining

l. Semantic-based Data Mining and Data Pre-processing

m. Mobility and Big Data

n. Multimedia and Multi-structured Data- Big Variety Data



5. Ethics, Privacy and Trust in Big Data Systems

a. Techniques and models for fairness and diversityÿ

b. Experimental studies of fairness, diversity, accountability, and transparencyÿ

c. Techniques and models for transparency and interpretabilityÿ

d. Trade-offs between transparency and privacy

e. Intrusion Detection for Gigabit Networks

f. Anomaly and APT Detection in Very Large Scale Systems

g. High Performance Cryptography

h. Visualizing Large Scale Security Data

i. Threat Detection using Big Data Analytics

j. Privacy Preserving Big Data Collection/Analytics

k. HCI Challenges for Big Data Security & Privacy

l. Trust management in IoT and other Big Data Systems





6. Hardware/OSÿAcceleration for Big Data

a. FPGA/CGRA/GPU accelerators for Big Data applications

b. Operating system support and runtimes for hardware accelerators

c. Programming models and platforms for accelerators

d. Domain-specific and heterogeneous architectures

e. Novel system organizations and designs

f. Computation in memory/storage/network

g. Persistent, non-volatile and emerging memory for Big Data

h. Operating system support for high-performance network architectures



7. Big Data Applications

a. Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication

b. Big Data Analytics in Small Business Enterprises (SMEs),

c. Big Data Analytics in Government, Public Sector and Society in General

d. Real-life Case Studies of Value Creation through Big Data Analytics

e. Big Data as a Service

f. Big Data Industry Standards

g. 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,