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
*

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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