IEEE Big Data 2017 Call for Papers

2017 IEEE International Conference on Big Data(Big Data 2017)
http://cci.drexel.edu/bigdata/bigdata2017/
December 11-14, 2017, Boston, MA, 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. (Big Data 2013) The regular paper acceptance rate is 17.0%
  • The IEEE Big Data 2016( regular paper acceptance rate: 18.7%) was held in Washington DC, Dec 5-8, 2016 with close to 900 registered participants from 43 countries.

The 2017 IEEE International Conference on Big Data (IEEE Big Data 2017) 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 2017 will offer student travel to student authors (including post-docs)

    Journal Publication

    A set of about 10 papers will be selected for a fast-track review and then published at the IEEE Transactions on Big Data.

    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: Aug. 20, 2017
  • Notification of paper acceptance: Oct.9, 2017
  • Camera-ready of accepted papers: Nov.10, 2017
  • Conference: Dec.11-14, 2017

    • Conference Co-Chairs

    Dr. Ricardo Baeza-Yates : NTENT, USA
    Prof. Xiaohua Tony Hu : Drexel University, USA
    Dr. Jeremy Kepner : MIT Lincoln Laboratory, USA

    Program Co-Chairs

    Prof. Jian-Yun Nie : University of Montreal, Canada
    Prof. Zoran Obradovic : Temple University, USA
    Dr. Toyotaro Suzumura : IBM T.J. Watson Research Center, USA

    Industry and Government Program Committee Chairs

    Dr. Raghunath Nambiar : CISCO, USA
    Dr. Sudarsan Rachuri : Dept of Energy, USA
    Dr. Rumi Ghosh : BOSCH, USA
    Dr. Chonggang Wang : InterDigital Inc., USA
    Dr. Hui Zang : Huawei Research, USA