IEEE BigData 2019: Difference between revisions

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The IEEE Big Data conference series started in 2013 has established itself as the top tier research conference in Big Data.
  Example topics of interest includes but is not limited to the following:
 
*
     The first conference IEEE Big Data 2013 (regular paper acceptance rate: 17.0%) was held in Santa Clara, CA from Oct 6-9, 2013 with more than 400 registered participants from 40 countries.
*    Big Data Science and Foundations
    The IEEE Big Data 2014 (regular paper acceptance rate: 18.5.0%) was held in Washington DC, Oct 27-30, 2014 with more than 600 registered participants from 45 countries.
*        Novel Theoretical Models for Big Data
     The IEEE Big Data 2015 (regular paper acceptance rate: 16.8%) was held in Santa Clara, Oct 29-Nov 1, 2015 with more than 780 registered participants from 49 countries.
*        New Computational Models for Big Data
     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.
*        Data and Information Quality for Big Data
     The IEEE Big Data 2017 (regular paper acceptance rate: 17.8%) was held in Boston, MA, Dec 11-14, 2017 with close to 1000 registered participants from 50 countries.
*        New Data Standards
    The IEEE Big Data 2018 (regular paper acceptance rate: 19.7%) was held in Seattle, WA, Dec 10-13, 2018 with close to 1100 registered participants from 47 countries.
*    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 a variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
*        Algorithms and Systems for Big DataSearch
*        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
*     Ethics, Privacy and Trust in Big Data Systems
*        Techniques and models for fairness and diversity
*        Experimental studies of fairness, diversity, accountability, and transparency
*        Techniques and models for transparency and interpretability
*        Trade-offs between transparency and privacy
*        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 Preserving Big Data Collection/Analytics
*        HCI Challenges for Big Data Security & Privacy
*        Trust management in IoT and other Big Data Systems
*     Hardware/OS Acceleration for Big Data
*        FPGA/CGRA/GPU accelerators for Big Data applications
*        Operating system support and runtimes for hardware accelerators
*        Programming models and platforms for accelerators
*        Domain-specific and heterogeneous architectures
*        Novel system organizations and designs
*        Computation in memory/storage/network
*        Persistent, non-volatile and emerging memory for Big Data
*        Operating system support for high-performance network architectures
*     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

Latest revision as of 19:14, 18 May 2020

IEEE BigData 2019
IEEE International Conference on Big Data
Event in series IEEE BigData
Dates 2019/12/09 (iCal) - 2019/12/12
Homepage: http://bigdataieee.org/BigData2019/
Location
Location: Los Angeles, California, USA
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Committees
General chairs: Roger Barga, Carlo Zaniolo
Table of Contents 


 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 a variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
  • Algorithms and Systems for Big DataSearch
  • 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
  • Ethics, Privacy and Trust in Big Data Systems
  • Techniques and models for fairness and diversity
  • Experimental studies of fairness, diversity, accountability, and transparency
  • Techniques and models for transparency and interpretability
  • Trade-offs between transparency and privacy
  • 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 Preserving Big Data Collection/Analytics
  • HCI Challenges for Big Data Security & Privacy
  • Trust management in IoT and other Big Data Systems
  • Hardware/OS Acceleration for Big Data
  • FPGA/CGRA/GPU accelerators for Big Data applications
  • Operating system support and runtimes for hardware accelerators
  • Programming models and platforms for accelerators
  • Domain-specific and heterogeneous architectures
  • Novel system organizations and designs
  • Computation in memory/storage/network
  • Persistent, non-volatile and emerging memory for Big Data
  • Operating system support for high-performance network architectures
  • 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
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