The 6th IEEE International Conference on Big Data (Big Data 2018)

December 10-13, 2018

Seattle, WA, USA

http://cci.drexel.edu/bigdata/bigdata2018/index.html

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 ( Big Data 2013) and the regular paper acceptance  rate is 17.0%.
  • The IEEE Big Data 2017 ( IEEE Big Data 2016 ,  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 2018 IEEE International Conference on Big Data (IEEE Big Data 2018) 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. Example topics of interest includes but is not limited to the following:

Big Data Science and Foundations

  • Novel Theoretical Models for Big Data
  • New Computational Models for Big Data
  • Data and Information Quality for Big Data
  • New Data Standards

Big Data Infrastructure

  • Cloud/Grid/Stream Computing for Big Data
  • High Performance/Parallel Computing Platforms for Big Data
  • Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment
  • Energy-efficient Computing for Big Data
  • Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data
  • Software Techniques and Architectures in Cloud/Grid/Stream Computing
  • Big Data Open Platforms
  • New Programming Models for Big Data beyond Hadoop/MapReduce, STORM
  • Software Systems to Support Big Data Computing

Big Data Management

  • Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
  • Algorithms and Systems for Big Data Search
  • Distributed, and Peer-to-peer Search
  • Big Data Search Architectures, Scalability and Efficiency
  • Data Acquisition, Integration, Cleaning, and Best Practices
  • Visualization Analytics for Big Data
  • Computational Modeling and Data Integration
  • Large-scale Recommendation Systems and Social Media Systems
  • Cloud/Grid/Stream Data Mining- Big Velocity Data
  • Link and Graph Mining
  • Semantic-based Data Mining and Data Pre-processing
  • Mobility and Big Data
  • Multimedia and Multi-structured Data- Big Variety Data

Big Data Search and Mining

  • Social Web Search and Mining
  • Web Search
  • Algorithms and Systems for Big Data Search
  • Distributed, and Peer-to-peer Search
  • Big Data Search Architectures, Scalability and Efficiency
  • Data Acquisition, Integration, Cleaning, and Best Practices
  • Visualization Analytics for Big Data
  • Computational Modeling and Data Integration
  • Large-scale Recommendation Systems and Social Media Systems
  • Cloud/Grid/StreamData Mining- Big Velocity Data
  • Link and Graph Mining
  • Semantic-based Data Mining and Data Pre-processing
  • Mobility and Big Data
  • Multimedia and Multi-structured Data-Big Variety Data

Big Data Security, Privacy and Trust

  • Intrusion Detection for Gigabit Networks
  • Anomaly and APT Detection in Very Large Scale Systems
  • High Performance Cryptography
  • Visualizing Large Scale Security Data
  • Threat Detection using Big Data Analytics
  • Privacy Threats of Big Data
  • Privacy Preserving Big Data Collection/Analytics
  • HCI Challenges for Big Data Security & Privacy
  • User Studies for any of the above
  • Sociological Aspects of Big Data Privacy
  • Trust management in IoT and other Big Data Systems

Big Data Applications

  • Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication
  • Big Data Analytics in Small Business Enterprises (SMEs)
  • Big Data Analytics in Government, Public Sector and Society in General
  • Real-life Case Studies of Value Creation through Big Data Analytics
  • Big Data as a Service
  • Big Data Industry Standards
  • 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 2018 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.
Paper Submission Page 
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 8, 2018
  • Notification of paper acceptance: Oct 9, 2018
  • Camera-ready of accepted papers: Nov 10, 2018
  • Conference: Dec 10-13, 2018

Committee Members

Conference Co-Chairs

  • Dr. Donald Kossmann : Microsoft Research, USA
  • Prof. Bing Liu : University of Illinois at Chicago, USA

Program Co-Chairs

  • Dr. Naoki Abe : IBM T.J. Watson Research Center, USA
  • Prof. Huan Liu : Arizona State University, USA
  • Prof. Calton Pu : Georgia Institute of Technology, USA

Industry and Government Program Committee Co-Chairs

  • Dr. Nesreen Ahmed : Intel Labs, USA
  • Dr. Mu Qiao : IBM Almaden Research Center, USA
  • Dr. Yang Song : Google Seattle Lab, USA

Workshop Co-Chairs

  • Prof. Jingrui He : Arizona State University, USA
  • Prof. Jeffrey Saltz : Syracuse University, USA

Tutorial Co-Chairs

  • Prof. Honggang Wang : University of Massachusetts, Dartmouth, USA
  • Dr. Lingfei (Teddy) Wu : IBM Research AI, USA

Big Data Cup Chairs

  • Prof. Mohammad Al Hasan (Hasan) : IUPUI, USA
  • Prof. Yicheng Tu : South Florida University, USA

Poster Chairs

  • Prof. Michael Gubanov : University of Texas at San Antonio, USA
  • Prof. Xia (Ben) Hu : Texas A&M University, USA

Local Arrangement Chairs

  • Dr. Mengwen Liu : Amazon, USA
  • Dr. Zunyan Xiong : T-Mobile, USA

Registration Chair

  • Prof. Yuan An : Drexel University, USA

Publicity Chairs

  • Prof. Hyoil Han : Illinois State University, USA
  • Prof. Alfredo Cuzzocrea : University of Trieste, Italy

BigData Steering Committee

  • Dr. Amr Awadallah : Cloudera, USA
  • Dr. Xueqi Cheng : Chinese Academy of Science, China
  • Prof. Yi-ke Guo : Imperial College, UK
  • Prof. Jimmy Lin : University of Waterloo, Canada
  • Prof. Xiaohua Tony Hu (Chair) (xh29@drexel.edu) : Drexel University, USA
  • Dr. Raghunath Nambiar : Cisco Systems, USA
  • Prof. Jian Pei : Simon Fraser University, Canada
  • Prof. Vijay Raghavan : University of Louisiana at Lafayette, USA
  • Prof. Amit Sheth : Wright State University, USA
  • Prof. Matthew Smith : Leibniz Universität Hannove, Germany
  • Dr. Shusaku Tsumoto : Shimane University, Japan
  • Prof. Athanasios Vasilakos : Lulea University of Technology(LTU), Sweden
  • Prof. Qiang Yang : Hong Kong University of Science and Technology, China
  • Pro. Wei Wang : University of California at Los Angle, USA