Difference between revisions of "Event:IEEE BigData 2019"

From ConfIDent
(mobo import Concept___Event_For_Confident-migrated)
 
 
(7 intermediate revisions by 2 users not shown)
Line 1: Line 1:
 
{{Event
 
{{Event
−
|Acronym=IEEE BigData 2019
+
|Acronym=BigData 2019
−
|Title=IEEE International Conference on Big Data
+
|Title=2019 IEEE International Conference on Big Data
 +
|Ordinal=7
 +
|In Event Series=Event Series:IEEE BigData
 +
|Single Day Event=no
 +
|Start Date=2019/12/09
 +
|End Date=2019/12/12
 +
|Event Status=as scheduled
 +
|Event Mode=on site
 +
|Venue=Westin Bonaventure
 +
|City=Los Angeles
 +
|Region=California
 +
|Country=Country:US
 +
|Academic Field=Big Data; Computer Science
 +
|Official Website=http://bigdataieee.org/BigData2019/
 +
|DOI=10.25798/xsvq-fj37
 
|Type=Conference
 
|Type=Conference
−
|Homepage=http://bigdataieee.org/BigData2019/
 
−
|City=Los Angeles
 
−
|State=California
 
−
|Country=USA
 
 
|has general chair=Roger Barga, Carlo Zaniolo
 
|has general chair=Roger Barga, Carlo Zaniolo
 
|has Proceedings Link=https://ieeexplore.ieee.org/xpl/conhome/8986695/proceeding
 
|has Proceedings Link=https://ieeexplore.ieee.org/xpl/conhome/8986695/proceeding
Line 12: Line 22:
 
|pageEditor=User:Curator 27
 
|pageEditor=User:Curator 27
 
|contributionType=1
 
|contributionType=1
−
|In Event Series=Event Series:IEEE BigData
 
−
|Single Day Event=no
 
−
|Start Date=2019/12/09
 
−
|End Date=2019/12/12
 
 
}}
 
}}
−
  Example topics of interest includes but is not limited to the following:
+
{{Event Deadline}}
−
*  
+
{{Organizer
−
*     Big Data Science and Foundations
+
|Contributor Type=organization
−
*         Novel Theoretical Models for Big Data
+
|Organization=Institute of Electrical and Electronics Engineers
−
*         New Computational Models for Big Data
+
}}
−
*         Data and Information Quality for Big Data
+
{{Event Metric}}
−
*         New Data Standards
+
{{S Event}}
−
*     Big Data Infrastructure
+
Example topics of interest includes but is not limited to the following:
−
*         Cloud/Grid/Stream Computing for Big Data
+
*
−
*         High Performance/Parallel Computing Platforms for Big Data
+
*Big Data Science and Foundations
−
*         Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment
+
*Novel Theoretical Models for Big Data
−
*         Energy-efficient Computing for Big Data
+
*New Computational Models for Big Data
−
*         Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data
+
*Data and Information Quality for Big Data
−
*         Software Techniques and Architectures in Cloud/Grid/Stream Computing
+
*New Data Standards
−
*         Big Data Open Platforms
+
*Big Data Infrastructure
−
*         New Programming Models for Big Data beyond Hadoop/MapReduce, STORM
+
*Cloud/Grid/Stream Computing for Big Data
−
*         Software Systems to Support Big Data Computing
+
*High Performance/Parallel Computing Platforms for Big Data
−
*     Big Data Management
+
*Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment
−
*         Search and Mining of a variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
+
*Energy-efficient Computing for Big Data
−
*         Algorithms and Systems for Big DataSearch
+
*Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data
−
*         Distributed, and Peer-to-peer Search
+
*Software Techniques and Architectures in Cloud/Grid/Stream Computing
−
*         Big Data Search Architectures, Scalability and Efficiency
+
*Big Data Open Platforms
−
*         Data Acquisition, Integration, Cleaning, and Best Practices
+
*New Programming Models for Big Data beyond Hadoop/MapReduce, STORM
−
*         Visualization Analytics for Big Data
+
*Software Systems to Support Big Data Computing
−
*         Computational Modeling and Data Integration
+
*Big Data Management
−
*         Large-scale Recommendation Systems and Social Media Systems
+
*Search and Mining of a variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
−
*         Cloud/Grid/Stream Data Mining- Big Velocity Data
+
*Algorithms and Systems for Big DataSearch
−
*         Link and Graph Mining
+
*Distributed, and Peer-to-peer Search
−
*         Semantic-based Data Mining and Data Pre-processing
+
*Big Data Search Architectures, Scalability and Efficiency
−
*         Mobility and Big Data
+
*Data Acquisition, Integration, Cleaning, and Best Practices
−
*         Multimedia and Multi-structured Data- Big Variety Data
+
*Visualization Analytics for Big Data
−
*     Big Data Search and Mining
+
*Computational Modeling and Data Integration
−
*         Social Web Search and Mining
+
*Large-scale Recommendation Systems and Social Media Systems
−
*         Web Search
+
*Cloud/Grid/Stream Data Mining- Big Velocity Data
−
*         Algorithms and Systems for Big Data Search
+
*Link and Graph Mining
−
*         Distributed, and Peer-to-peer Search
+
*Semantic-based Data Mining and Data Pre-processing
−
*         Big Data Search Architectures, Scalability and Efficiency
+
*Mobility and Big Data
−
*         Data Acquisition, Integration, Cleaning, and Best Practices
+
*Multimedia and Multi-structured Data- Big Variety Data
−
*         Visualization Analytics for Big Data
+
*Big Data Search and Mining
−
*         Computational Modeling and Data Integration
+
*Social Web Search and Mining
−
*         Large-scale Recommendation Systems and Social Media Systems
+
*Web Search
−
*         Cloud/Grid/StreamData Mining- Big Velocity Data
+
*Algorithms and Systems for Big Data Search
−
*         Link and Graph Mining
+
*Distributed, and Peer-to-peer Search
−
*         Semantic-based Data Mining and Data Pre-processing
+
*Big Data Search Architectures, Scalability and Efficiency
−
*         Mobility and Big Data
+
*Data Acquisition, Integration, Cleaning, and Best Practices
−
*         Multimedia and Multi-structured Data- Big Variety Data
+
*Visualization Analytics for Big Data
−
*     Ethics, Privacy and Trust in Big Data Systems
+
*Computational Modeling and Data Integration
−
*         Techniques and models for fairness and diversity
+
*Large-scale Recommendation Systems and Social Media Systems
−
*         Experimental studies of fairness, diversity, accountability, and transparency
+
*Cloud/Grid/StreamData Mining- Big Velocity Data
−
*         Techniques and models for transparency and interpretability
+
*Link and Graph Mining
−
*         Trade-offs between transparency and privacy
+
*Semantic-based Data Mining and Data Pre-processing
−
*         Intrusion Detection for Gigabit Networks
+
*Mobility and Big Data
−
*         Anomaly and APT Detection in Very Large Scale Systems
+
*Multimedia and Multi-structured Data- Big Variety Data
−
*         High Performance Cryptography
+
*Ethics, Privacy and Trust in Big Data Systems
−
*         Visualizing Large Scale Security Data
+
*Techniques and models for fairness and diversity
−
*         Threat Detection using Big Data Analytics
+
*Experimental studies of fairness, diversity, accountability, and transparency
−
*         Privacy Preserving Big Data Collection/Analytics
+
*Techniques and models for transparency and interpretability
−
*         HCI User:Curator 83allenges for Big Data Security & Privacy
+
*Trade-offs between transparency and privacy
−
*         Trust management in IoT and other Big Data Systems
+
*Intrusion Detection for Gigabit Networks
−
*     Hardware/OS Acceleration for Big Data
+
*Anomaly and APT Detection in Very Large Scale Systems
−
*         FPGA/CGRA/GPU accelerators for Big Data applications
+
*High Performance Cryptography
−
*         Operating system support and runtimes for hardware accelerators
+
*Visualizing Large Scale Security Data
−
*         Programming models and platforms for accelerators
+
*Threat Detection using Big Data Analytics
−
*         Domain-specific and heterogeneous architectures
+
*Privacy Preserving Big Data Collection/Analytics
−
*         Novel system organizations and designs
+
*HCI Challenges for Big Data Security & Privacy
−
*         Computation in memory/storage/network
+
*Trust management in IoT and other Big Data Systems
−
*         Persistent, non-volatile and emerging memory for Big Data
+
*Hardware/OS Acceleration for Big Data
−
*         Operating system support for high-performance network architectures
+
*FPGA/CGRA/GPU accelerators for Big Data applications
−
*     Big Data Applications
+
*Operating system support and runtimes for hardware accelerators
−
*         Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication
+
*Programming models and platforms for accelerators
−
*         Big Data Analytics in Small Business Enterprises (SMEs),
+
*Domain-specific and heterogeneous architectures
−
*         Big Data Analytics in Government, Public Sector and Society in General
+
*Novel system organizations and designs
−
*         Real-life Case Studies of Value Creation through Big Data Analytics
+
*Computation in memory/storage/network
−
*         Big Data as a Service
+
*Persistent, non-volatile and emerging memory for Big Data
−
*         Big Data Industry Standards
+
*Operating system support for high-performance network architectures
−
*         Experiences with Big Data Project Deployments
+
*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 17:08, 22 November 2023

The document "BigData 2019" was published on "2023-11-22T17:08:46" on the website "ConfIDent" under the URL https://confident-conference.org/index.php/Event:IEEE BigData 2019.
The document "BigData 2019" describes an event in the sense of a conference.
The document "BigData 2019" contains information about the event "BigData 2019" with start date "2019/12/09" and end date "2019/12/12".
The event "BigData 2019" is part of the event series identified by [[Event Series:IEEE BigData]]
Deadlines
organization
Metrics
Venue

Westin Bonaventure, Los Angeles, California, United States of America

Loading map...

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.confident-conference.org:443/index.php?title=Event:IEEE_BigData_2019&oldid=124287"
Cookies help us deliver our services. By using our services, you agree to our use of cookies.
More information