The 1st IEEE International Conference on Big Data (IEEE Big Data 2013)

The 1st IEEE International Conference on Big Data (IEEE Big Data 2013)

http://www.ischool.drexel.edu/bigdata/bigdata2013/

October 6-9, 2013

Santa Clara, 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 society itself. The IEEE International Conference on Big Data 2013 (IEEE BigData 2013) provides a leading forum for disseminating the latest research in Big Data Research, Development, and Applications.

We solicit high-quality original research papers (including significant work-in-progress) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity). big data science and foundations, big data infrastructure, big data management, big data searching and mining, big data privacy/security, and big data applications. Relevant topics include but are not limited to:

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 andArchitectures 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).

Organizing Committee

Conference Co-Chairs

Prof. T.Y. Lin, San Jose State University, USA
Prof. Vijay Raghavan, Univ. of Louisiana at Lafayette, USA,
Prof. Benjamin Wah, Chinese Univ. of Hong Kong, China

Program Co-Chairs

Dr. Ricardo Baeza-Yates, Yahoo! Labs, Spain
Prof. Geoffrey Fox, Indiana University, USA
Prof. Cyrus Shahabi, University of Southern California, USA
Prof. Matthew Smith, Leibniz Universität Hannove, Germany
Dr. Qiang Yang, Huawei Noah’s Ark Lab, China

Industry and Government Program Committee Chairs

Mr. Rayid Ghani, Obama for America
Dr. Wei Han, Huawei Noah Ark Lab, China
Dr. Ronny Lempel, Yahoo! Labs, Israel
Dr. Raghunath Nambiar, Cisco Systems, USA

BigData Steering Committee Chair

Prof. Xiaohua Tony Hu, Drexel University, USA, thu@cis.drexel.edu

Paper Submission

Please submit a full-length paper (upto 9 page IEEE 2-column format) through the online submission system:
http://wi-lab.com/cyberchair/2013/bigdata13/cbc_index.html.
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 June 23, 2013
Notification of paper acceptance Aug 10, 2013
Camera-ready of accepted papers Sept 1, 2013
Conference October 6-9, 2013