The 9th IEEE International Conference on Big Data (IEEE BigData 2021)

December 15-18, 2021

http://bigdataieee.org/BigData2021/

IEEE BigData 2021 – Now Taking Place Virtually

The safety and well-being of all conference participants is our priority. After evaluating the current COVID-19 situation and the restriction of international travel, the decision has been made to transform the in-person component of IEEE BigData 2021 into an all-digital conference experience – IEEE BigData 2021 will now be an online event. Therefore, IEEE BigData 2021 will no longer take place in Orlando, FL, US and will instead take place virtually. The conference dates remain the same – December 15-18, 2021. Proceedings will not be cancelled, and publications will continue as planned.

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 2019 ( http://bigdataieee.org/BigData2019/ , regular paper acceptance rate: 18.7%) was held in Los Angeles, CA, Dec 9-12, 2019 with close to 1200 registered participants from 54 countries.
  • The IEEE Big Data 2020 ( http://bigdataieee.org/BigData2020/ , regular paper acceptance rate: 15.7%) was held online, Dec 10-13, 2020 with close to 1100 registered participants from 50 countries.

The 2021 IEEE International Conference on Big Data (IEEE BigData 2021) 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

Novel Theoretical Models for Big DataNew Computational Models for Big DataData and Information Quality for Big DataNew Data Standards

2. Big Data Infrastructure

Cloud/Grid/Stream Computing for Big DataHigh Performance/Parallel Computing Platforms for Big DataAutonomic Computing and Cyber-infrastructure, System Architectures, Design and DeploymentEnergy-efficient Computing for Big DataProgramming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big DataSoftware Techniques and Architectures in Cloud/Grid/Stream ComputingBig Data Open PlatformsNew Programming Models for Big Data beyond Hadoop/MapReduce, STORMSoftware Systems to Support Big Data Computing

3. Big Data Management

Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia dataAlgorithms and Systems for Big Data SearchDistributed, and Peer-to-peer SearchBig Data Search Architectures, Scalability and EfficiencyData Acquisition, Integration, Cleaning, and Best PracticesVisualization Analytics for Big DataComputational Modeling and Data IntegrationLarge-scale Recommendation Systems and Social Media SystemsCloud/Grid/Stream Data Mining- Big Velocity DataLink and Graph MiningSemantic-based Data Mining and Data Pre-processingMobility and Big DataMultimedia and Multi-structured Data- Big Variety Data

4. Big Data Search and Mining

Social Web Search and MiningWeb SearchAlgorithms and Systems for Big Data SearchDistributed, and Peer-to-peer SearchBig Data Search Architectures, Scalability and EfficiencyData Acquisition, Integration, Cleaning, and Best PracticesVisualization Analytics for Big DataComputational Modeling and Data IntegrationLarge-scale Recommendation Systems and Social Media SystemsCloud/Grid/StreamData Mining- Big Velocity DataLink and Graph MiningSemantic-based Data Mining and Data Pre-processingMobility and Big DataMultimedia and Multi-structured Data-Big Variety Data

5. Big Data Learning and Analytics

Predictive analytics on Big DataMachine learning algorithms for Big DataDeep learning for Big DataFeature representation learning for Big DataDimension redution for Big DataPhysics informed Big Data learning

6. Ethics, Privacy and Trust in Big Data Systems

Techniques and models for fairness and diversityExperimental studies of fairness, diversity, accountability, and transparencyTechniques and models for transparency and interpretabilityTrade-offs between transparency and privacyIntrusion Detection for Gigabit NetworksAnomaly and APT Detection in Very Large Scale SystemsHigh Performance CryptographyVisualizing Large Scale Security DataThreat Detection using Big Data AnalyticsPrivacy Preserving Big Data Collection/AnalyticsHCI Challenges for Big Data Security & PrivacyTrust management in IoT and other Big Data Systems

7. Hardware/OS Acceleration for Big Data

FPGA/CGRA/GPU accelerators for Big Data applicationsOperating system support and runtimes for hardware acceleratorsProgramming models and platforms for acceleratorsDomain-specific and heterogeneous architecturesNovel system organizations and designsComputation in memory/storage/networkPersistent, non-volatile and emerging memory for Big DataOperating system support for high-performance network architectures

8. Big Data Applications

Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, TelecommunicationBig Data Analytics in Small Business Enterprises (SMEs)Big Data Analytics in Government, Public Sector and Society in GeneralReal-life Case Studies of Value Creation through Big Data AnalyticsBig Data as a ServiceBig Data Industry StandardsExperiences 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 thepractical, 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 2021 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, reference pages don’t counted in the 10 pages) through the online submission system.
https://wi-lab.com/cyberchair/2021/bigdata21/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: September 5, 2021
  • Notification of paper acceptance: Oct 27, 2021
  • Camera-ready of accepted papers: Nov 15, 2021
  • Conference: Dec 15-18, 2021

Organizing Committee Members

Conference Co-Chairs

  • Usama Fayyad, Open Insight & Northeastern University, USA
  • Xingquan Zhu, Florida Atlantic University, USA

Program Co-Chairs

  • Yixin Chen, Washington University at St Louis, USA
  • Heiko Ludwig, IBM Almaden Research Center, USA
  • Yicheng Tu, South Florida University, USA

Vice Chairs in Big Data Science and Foundations

  • Muhan Zhang, Facebook AI Applied Research, USA
  • Jun Zhu, Tsinghua University, China

Vice Chairs in Big Data Infrastructure

  • Ritu Arora, University of Teas at San Antonio, USA
  • Suren Byna, Lawrence Berkeley National Laboratory, USA

Vice Chairs in Big Data Management

  • Michael Gubanov, Florida State University, USA
  • Vassilis Tsotras, University of California at Riverside, USA

Vice Chairs in Big Data Search and Mining

  • Yizhou Sun, University of California at Los Angeles, USA
  • Guobing Zou, Shanghai University, China

Vice Chairs in Big Data Learning and Analytics

  • Hanghang Tong, UIUC, USA
  • My T. Thai, University of Florida, USA

Vice Chairs in Big Data Security, Privacy and Trust

  • Netanel Raviv, Washington University at St Louis, USA
  • Francesca Rossi, IBM, USA

Vice Chairs in Hardware/OS Accelerating for Big Data

  • Mohammad Sadoghi, University of California at Davis, USA
  • Zichen Xu, Nanchang University, China

Vice Chairs in Big Data Applications

  • Balaji Palanisamy, University of Pittsburgh, USA
  • Xiaoyan Zhu, Tsinghua University, China

Industry and Government Program Co-Chairs

  • Suren Byna, Lawrence Berkeley National Laboratory, USA
  • Xiong Liu, Novartis, USA
  • Jianping Zhang, Ankura.com, USA

Workshop Co-Chairs

  • Shirui Pan, Monash University, Australia
  • Vagelis Papalexakis, University of California at Riverside, USA
  • Jianwu Wang, Univ. of Maryland at Baltimore County, USA

Tutorial Co-Chairs

  • Ali R Butt, Virginia Tech, USA
  • Jia Wu, Macquarie University, Australia

Proceedings Co-Chairs

  • Alfredo Cuzzocrea, University of Calabria, Italy
  • Carlos Ordonez, University of Houston , USA

Big Data Cup Co-Chairs

  • Shaun Canavan, University of South Florida, USA
  • Yanjie Fu, University of Central Florida, USA

Big Data Sponsorship Co-Chairs

  • Jie Cao, Nanjing University of Finance and Economics, China

Big Data Poster Co-Chairs

  • Yufei Tang, Florida Atlantic University, USA
  • Ladjel Bellatreche , ÉCOLE NATIONALE SUPÉRIEURE DE MÉCANIQUE ET D’AÉROTECHNIQUE, France

Local Arrangements Chair

  • Yogesh S Rawat, University of Central Florida, USA

Registration Chair

  • Liqiang (Eric) Wang, University of Central Florida, USA

Steering Committee Chair

  • Xiaohua Tony Hu, Drexel University, USA