Difference between revisions of "IEEE BigData 2019"

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  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

Latest revision as of 20:14, 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
Retrieved from "https://www.openresearch.org/mediawiki/index.php?title=IEEE_BigData_2019&oldid=222862"
Facts about "IEEE BigData 2019"
RDF feed
AcronymIEEE BigData 2019 +
End dateDecember 12, 2019 +
Event in seriesIEEE BigData +
Event typeConference +
Has coordinates34° 3' 13", -118° 14' 34"Latitude: 34.053691666667
Longitude: -118.24276666667 +
Has general chairRoger Barga + and Carlo Zaniolo +
Has location cityLos Angeles +
Has location countryCategory:USA +
Has location stateCalifornia +
Homepagehttp://bigdataieee.org/BigData2019/ +
IsAEvent +
Start dateDecember 9, 2019 +
TitleIEEE International Conference on Big Data +