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|Acronym=BIGCOMP 2020 | |Acronym=BIGCOMP 2020 | ||
|Title=2020 IEEE International Conference on Big Data | |Title=2020 IEEE International Conference on Big Data | ||
| − | | | + | |In Event Series=Event Series:BIGCOMP |
| − | | | + | |Single Day Event=no |
| + | |Start Date=2020/12/10 | ||
| + | |End Date=2020/12/13 | ||
| + | |Event Status=as scheduled | ||
| + | |Event Mode=on site | ||
|City=Atlanta | |City=Atlanta | ||
|Region=GA | |Region=GA | ||
|Country=Country:US | |Country=Country:US | ||
| − | | | + | |Academic Field=Artificial Intelligence; Big Data; Cloud Computing; Database |
| + | |Official Website=http://bigdataieee.org/BigData2020/index.html | ||
| + | |DOI=10.25798/am8d-t769 | ||
| + | |Type=Conference | ||
|Has coordinator=Yubao Wu | |Has coordinator=Yubao Wu | ||
|has general chair=Srinivas Aluru, Chengxiang Zhai | |has general chair=Srinivas Aluru, Chengxiang Zhai | ||
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|pageEditor=User:Curator 19 | |pageEditor=User:Curator 19 | ||
|contributionType=1 | |contributionType=1 | ||
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}} | }} | ||
{{Event Deadline | {{Event Deadline | ||
| + | |Notification Deadline=2020/10/16 | ||
|Paper Deadline=2020/08/19 | |Paper Deadline=2020/08/19 | ||
| − | |||
|Camera-Ready Deadline=2020/11/10 | |Camera-Ready Deadline=2020/11/10 | ||
}} | }} | ||
| + | {{Organizer | ||
| + | |Contributor Type=organization | ||
| + | |Organization=IEEE Computer Society | ||
| + | }} | ||
| + | {{Event Metric}} | ||
{{S Event}} | {{S Event}} | ||
'''IEEE Big Data 2020''' | '''IEEE Big Data 2020''' | ||
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1. '''Big Data Science and Foundations''' | 1. '''Big Data Science and Foundations''' | ||
| − | * Novel Theoretical Models for Big Data | + | *Novel Theoretical Models for Big Data |
| − | * New Computational Models for Big Data | + | *New Computational Models for Big Data |
| − | * Data and Information Quality for Big Data | + | *Data and Information Quality for Big Data |
| − | * New Data Standards | + | *New Data Standards |
2. '''Big Data Infrastructure''' | 2. '''Big Data Infrastructure''' | ||
| − | * Cloud/Grid/Stream Computing for Big Data | + | *Cloud/Grid/Stream Computing for Big Data |
| − | * High Performance/Parallel Computing Platforms for Big Data | + | *High Performance/Parallel Computing Platforms for Big Data |
| − | * Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment | + | *Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment |
| − | * Energy-efficient Computing for Big Data | + | *Energy-efficient Computing for Big Data |
| − | * Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support 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 | + | *Software Techniques and Architectures in Cloud/Grid/Stream Computing |
| − | * Big Data Open Platforms | + | *Big Data Open Platforms |
| − | * New Programming Models for Big Data beyond Hadoop/MapReduce, STORM | + | *New Programming Models for Big Data beyond Hadoop/MapReduce, STORM |
| − | * Software Systems to Support Big Data Computing | + | *Software Systems to Support Big Data Computing |
3. '''Big Data Management''' | 3. '''Big Data Management''' | ||
| − | * Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data | + | *Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data |
| − | * Algorithms and Systems for Big Data Search | + | *Algorithms and Systems for Big Data Search |
| − | * Distributed, and Peer-to-peer Search | + | *Distributed, and Peer-to-peer Search |
| − | * Big Data Search Architectures, Scalability and Efficiency | + | *Big Data Search Architectures, Scalability and Efficiency |
| − | * Data Acquisition, Integration, Cleaning, and Best Practices | + | *Data Acquisition, Integration, Cleaning, and Best Practices |
| − | * Visualization Analytics for Big Data | + | *Visualization Analytics for Big Data |
| − | * Computational Modeling and Data Integration | + | *Computational Modeling and Data Integration |
| − | * Large-scale Recommendation Systems and Social Media Systems | + | *Large-scale Recommendation Systems and Social Media Systems |
| − | * Cloud/Grid/Stream Data Mining- Big Velocity Data | + | *Cloud/Grid/Stream Data Mining- Big Velocity Data |
| − | * Link and Graph Mining | + | *Link and Graph Mining |
| − | * Semantic-based Data Mining and Data Pre-processing | + | *Semantic-based Data Mining and Data Pre-processing |
| − | * Mobility and Big Data | + | *Mobility and Big Data |
| − | * Multimedia and Multi-structured Data- Big Variety Data | + | *Multimedia and Multi-structured Data- Big Variety Data |
4. '''Big Data Search and Mining''' | 4. '''Big Data Search and Mining''' | ||
| − | * Social Web Search and Mining | + | *Social Web Search and Mining |
| − | * Web Search | + | *Web Search |
| − | * Algorithms and Systems for Big Data Search | + | *Algorithms and Systems for Big Data Search |
| − | * Distributed, and Peer-to-peer Search | + | *Distributed, and Peer-to-peer Search |
| − | * Big Data Search Architectures, Scalability and Efficiency | + | *Big Data Search Architectures, Scalability and Efficiency |
| − | * Data Acquisition, Integration, Cleaning, and Best Practices | + | *Data Acquisition, Integration, Cleaning, and Best Practices |
| − | * Visualization Analytics for Big Data | + | *Visualization Analytics for Big Data |
| − | * Computational Modeling and Data Integration | + | *Computational Modeling and Data Integration |
| − | * Large-scale Recommendation Systems and Social Media Systems | + | *Large-scale Recommendation Systems and Social Media Systems |
| − | * Cloud/Grid/StreamData Mining- Big Velocity Data | + | *Cloud/Grid/StreamData Mining- Big Velocity Data |
| − | * Link and Graph Mining | + | *Link and Graph Mining |
| − | * Semantic-based Data Mining and Data Pre-processing | + | *Semantic-based Data Mining and Data Pre-processing |
| − | * Mobility and Big Data | + | *Mobility and Big Data |
| − | * Multimedia and Multi-structured Data-Big Variety Data | + | *Multimedia and Multi-structured Data-Big Variety Data |
5. '''Ethics, Privacy and Trust in Big Data Systems''' | 5. '''Ethics, Privacy and Trust in Big Data Systems''' | ||
| − | * Techniques and models for fairness and diversity | + | *Techniques and models for fairness and diversity |
| − | * Experimental studies of fairness, diversity, accountability, and transparency | + | *Experimental studies of fairness, diversity, accountability, and transparency |
| − | * Techniques and models for transparency and interpretability | + | *Techniques and models for transparency and interpretability |
| − | * Trade-offs between transparency and privacy | + | *Trade-offs between transparency and privacy |
| − | * Intrusion Detection for Gigabit Networks | + | *Intrusion Detection for Gigabit Networks |
| − | * Anomaly and APT Detection in Very Large Scale Systems | + | *Anomaly and APT Detection in Very Large Scale Systems |
| − | * High Performance Cryptography | + | *High Performance Cryptography |
| − | * Visualizing Large Scale Security Data | + | *Visualizing Large Scale Security Data |
| − | * Threat Detection using Big Data Analytics | + | *Threat Detection using Big Data Analytics |
| − | * Privacy Preserving Big Data Collection/Analytics | + | *Privacy Preserving Big Data Collection/Analytics |
| − | * HCI Challenges for Big Data Security & Privacy | + | *HCI Challenges for Big Data Security & Privacy |
| − | * Trust management in IoT and other Big Data Systems | + | *Trust management in IoT and other Big Data Systems |
6. '''Hardware/OS Acceleration for Big Data''' | 6. '''Hardware/OS Acceleration for Big Data''' | ||
| − | * FPGA/CGRA/GPU accelerators for Big Data applications | + | *FPGA/CGRA/GPU accelerators for Big Data applications |
| − | * Operating system support and runtimes for hardware accelerators | + | *Operating system support and runtimes for hardware accelerators |
| − | * Programming models and platforms for accelerators | + | *Programming models and platforms for accelerators |
| − | * Domain-specific and heterogeneous architectures | + | *Domain-specific and heterogeneous architectures |
| − | * Novel system organizations and designs | + | *Novel system organizations and designs |
| − | * Computation in memory/storage/network | + | *Computation in memory/storage/network |
| − | * Persistent, non-volatile and emerging memory for Big Data | + | *Persistent, non-volatile and emerging memory for Big Data |
| − | * Operating system support for high-performance network architectures | + | *Operating system support for high-performance network architectures |
7. '''Big Data Applications''' | 7. '''Big Data Applications''' | ||
| − | * Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication | + | *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 Small Business Enterprises (SMEs) |
| − | * Big Data Analytics in Government, Public Sector and Society in General | + | *Big Data Analytics in Government, Public Sector and Society in General |
| − | * Real-life Case Studies of Value Creation through Big Data Analytics | + | *Real-life Case Studies of Value Creation through Big Data Analytics |
| − | * Big Data as a Service | + | *Big Data as a Service |
| − | * Big Data Industry Standards | + | *Big Data Industry Standards |
| − | * Experiences with Big Data Project Deployments | + | *Experiences with Big Data Project Deployments |
==Committees== | ==Committees== | ||
'''Conference Co Chairs''' | '''Conference Co Chairs''' | ||
| − | * Srinivas Aluru, Georgia Institute of Technology, USA | + | *Srinivas Aluru, Georgia Institute of Technology, USA |
*Chengxiang Zhai, Univ of Illinois at Urbana Champaign, USA | *Chengxiang Zhai, Univ of Illinois at Urbana Champaign, USA | ||
'''Program Co Chairs''' | '''Program Co Chairs''' | ||
*Chris Jermaine, Rice University, USA | *Chris Jermaine, Rice University, USA | ||
| − | * Xintao Wu, University of Arkansas, USA | + | *Xintao Wu, University of Arkansas, USA |
| − | * Li Xiong, Emory University, USA | + | *Li Xiong, Emory University, USA |
'''Vice Chairs in Big Data Science and Foundations''' | '''Vice Chairs in Big Data Science and Foundations''' | ||
| − | * Prof. Wei-Shinn Ku: Auburn University and National Science Foundation, USA | + | *Prof. Wei-Shinn Ku: Auburn University and National Science Foundation, USA |
| − | * Prof. Carlos Ordonez : University of Houston, USA | + | *Prof. Carlos Ordonez : University of Houston, USA |
'''Vice Chairs in Big Data Infrastructure''' | '''Vice Chairs in Big Data Infrastructure''' | ||
| − | * Prof. San-Woo Jun : University of California, Irvine, USA | + | *Prof. San-Woo Jun : University of California, Irvine, USA |
| − | * Prof. Jia Zou : Arizona State University, USA | + | *Prof. Jia Zou : Arizona State University, USA |
'''* Vice Chairs in Big Data Management''' | '''* Vice Chairs in Big Data Management''' | ||
| − | * Prof. Yannis Velegrakis : Utrecht University, Netherlands | + | *Prof. Yannis Velegrakis : Utrecht University, Netherlands |
| − | * Prof. Masatoshi Yoshikawa : Kyoto University, Japan | + | *Prof. Masatoshi Yoshikawa : Kyoto University, Japan |
'''Vice Chairs in Big Data Search and Mining''' | '''Vice Chairs in Big Data Search and Mining''' | ||
| − | * Prof. Prasenjit Mitra : Penn State University, USA | + | *Prof. Prasenjit Mitra : Penn State University, USA |
| − | * Prof. Chandan Reddy : Virginia Tech, USA | + | *Prof. Chandan Reddy : Virginia Tech, USA |
'''Vice Chairs in Big Data Security, Privacy and Trust''' | '''Vice Chairs in Big Data Security, Privacy and Trust''' | ||
| − | * Prof. Murat Kantarcioglu : University of Texas at Dallas, USA | + | *Prof. Murat Kantarcioglu : University of Texas at Dallas, USA |
| − | * Prof. Anna Squicciarini : Penn State University, USA | + | *Prof. Anna Squicciarini : Penn State University, USA |
'''Vice Chairs in Hardware/OS Accelerating for Big Data''' | '''Vice Chairs in Hardware/OS Accelerating for Big Data''' | ||
| − | * Prof. Spyro Blanas, Ohio State University, USA | + | *Prof. Spyro Blanas, Ohio State University, USA |
| − | * Prof. Kai Chen, Hong Kong University of Science and Technology, China | + | *Prof. Kai Chen, Hong Kong University of Science and Technology, China |
'''Vice Chairs in Big Data Applications''' | '''Vice Chairs in Big Data Applications''' | ||
| − | * Prof. Xiaoqian Jiang : University of Texas Health Science Center at Houston, USA | + | *Prof. Xiaoqian Jiang : University of Texas Health Science Center at Houston, USA |
| − | * Prof. Huzefa Rangwala : George Mason University, USA | + | *Prof. Huzefa Rangwala : George Mason University, USA |
'''Industry and Government Program Committee Co Chairs''' | '''Industry and Government Program Committee Co Chairs''' | ||
| − | * Olivera Kotevska, Oak Ridge National Laboratory (ORNL), USA | + | *Olivera Kotevska, Oak Ridge National Laboratory (ORNL), USA |
| − | * Siyuan Lu, IBM T.J. Watson Research center, USA | + | *Siyuan Lu, IBM T.J. Watson Research center, USA |
| − | * Weija Xu, Texas Advanced Computing Center, USA | + | *Weija Xu, Texas Advanced Computing Center, USA |
'''Workshop Co Chairs''' | '''Workshop Co Chairs''' | ||
| − | * Eyhab Ai-Masri, University of Washington, USA | + | *Eyhab Ai-Masri, University of Washington, USA |
| − | * Zhiyuan Chen, University of Maryland at Baltimore County, USA | + | *Zhiyuan Chen, University of Maryland at Baltimore County, USA |
| − | * Jeff Saltz, Syracuse University, USA | + | *Jeff Saltz, Syracuse University, USA |
'''Tutorial Co Chairs''' | '''Tutorial Co Chairs''' | ||
| − | * Rafal A. Angryk, Georgia State University, USA | + | *Rafal A. Angryk, Georgia State University, USA |
| − | * Hui Zhang, University of Louisville, USA | + | *Hui Zhang, University of Louisville, USA |
'''Big Data Cup Co Chairs''' | '''Big Data Cup Co Chairs''' | ||
| − | * Yicheng Tu, University of South Florida | + | *Yicheng Tu, University of South Florida |
| − | * Xingquan Zhu, Florida Atlantic University | + | *Xingquan Zhu, Florida Atlantic University |
'''Big Data Sponsorship Co Chairs''' | '''Big Data Sponsorship Co Chairs''' | ||
| − | * Dr. Erin-Elizabeth A. Durham, Georgia State University, USA (edurham@cs.gsu.edu) | + | *Dr. Erin-Elizabeth A. Durham, Georgia State University, USA (edurham@cs.gsu.edu) |
| − | * Xiaohua Tony Hu, Drexel University, USA (xh29@drexel.edu) | + | *Xiaohua Tony Hu, Drexel University, USA (xh29@drexel.edu) |
'''Local Arrangements Chair''' | '''Local Arrangements Chair''' | ||
| − | * Yubao Wu, Georgia State University, USA | + | *Yubao Wu, Georgia State University, USA |
'''Registration Chair''' | '''Registration Chair''' | ||
| − | * Berkay Aydin, Georgia State University, USA | + | *Berkay Aydin, Georgia State University, USA |
'''Steering Committee Chair''' | '''Steering Committee Chair''' | ||
| − | * Xiaohua Tony Hu, Drexel University, USA (xh29@drexel.edu) | + | *Xiaohua Tony Hu, Drexel University, USA (xh29@drexel.edu) |
==Important Dates== | ==Important Dates== | ||
| − | * Electronic submission of full papers: August 19, 2020 | + | *Electronic submission of full papers: August 19, 2020 |
| − | * Notification of paper acceptance: Oct 16, 2020 | + | *Notification of paper acceptance: Oct 16, 2020 |
| − | * Camera-ready of accepted papers: Nov 10, 2020 | + | *Camera-ready of accepted papers: Nov 10, 2020 |
| − | * Conference: Dec 10-13, 2020 | + | *Conference: Dec 10-13, 2020 |
Latest revision as of 06:28, 7 August 2023
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Venue
Atlanta, GA, United States of America
Warning: Venue is missing. The map might not show the exact location.
IEEE Big Data 2020
2020 IEEE International Conference on Big Data (IEEE BigData 2020) December 10-13, 2020, Atlanta, GA, USA
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 2018 ( http://bigdataieee.org/BigData2018/ , regular paper acceptance rate: 19.7%) was held in Seattle, WA, Dec 10-13, 2018 with close to 1100 registered participants from 47 countries. 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 2020 IEEE International Conference on Big Data (IEEE BigData 2020) 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 Atlanta this year.
TOPICS
Example topics of interest includes but is not limited to the following:
1. 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
2. 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
3. Big Data Management
- Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
- 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/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
4. 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
5. 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
6. 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
7. 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
Committees
Conference Co Chairs
- Srinivas Aluru, Georgia Institute of Technology, USA
- Chengxiang Zhai, Univ of Illinois at Urbana Champaign, USA
Program Co Chairs
- Chris Jermaine, Rice University, USA
- Xintao Wu, University of Arkansas, USA
- Li Xiong, Emory University, USA
Vice Chairs in Big Data Science and Foundations
- Prof. Wei-Shinn Ku: Auburn University and National Science Foundation, USA
- Prof. Carlos Ordonez : University of Houston, USA
Vice Chairs in Big Data Infrastructure
- Prof. San-Woo Jun : University of California, Irvine, USA
- Prof. Jia Zou : Arizona State University, USA
* Vice Chairs in Big Data Management
- Prof. Yannis Velegrakis : Utrecht University, Netherlands
- Prof. Masatoshi Yoshikawa : Kyoto University, Japan
Vice Chairs in Big Data Search and Mining
- Prof. Prasenjit Mitra : Penn State University, USA
- Prof. Chandan Reddy : Virginia Tech, USA
Vice Chairs in Big Data Security, Privacy and Trust
- Prof. Murat Kantarcioglu : University of Texas at Dallas, USA
- Prof. Anna Squicciarini : Penn State University, USA
Vice Chairs in Hardware/OS Accelerating for Big Data
- Prof. Spyro Blanas, Ohio State University, USA
- Prof. Kai Chen, Hong Kong University of Science and Technology, China
Vice Chairs in Big Data Applications
- Prof. Xiaoqian Jiang : University of Texas Health Science Center at Houston, USA
- Prof. Huzefa Rangwala : George Mason University, USA
Industry and Government Program Committee Co Chairs
- Olivera Kotevska, Oak Ridge National Laboratory (ORNL), USA
- Siyuan Lu, IBM T.J. Watson Research center, USA
- Weija Xu, Texas Advanced Computing Center, USA
Workshop Co Chairs
- Eyhab Ai-Masri, University of Washington, USA
- Zhiyuan Chen, University of Maryland at Baltimore County, USA
- Jeff Saltz, Syracuse University, USA
Tutorial Co Chairs
- Rafal A. Angryk, Georgia State University, USA
- Hui Zhang, University of Louisville, USA
Big Data Cup Co Chairs
- Yicheng Tu, University of South Florida
- Xingquan Zhu, Florida Atlantic University
Big Data Sponsorship Co Chairs
- Dr. Erin-Elizabeth A. Durham, Georgia State University, USA (edurham@cs.gsu.edu)
- Xiaohua Tony Hu, Drexel University, USA (xh29@drexel.edu)
Local Arrangements Chair
- Yubao Wu, Georgia State University, USA
Registration Chair
- Berkay Aydin, Georgia State University, USA
Steering Committee Chair
- Xiaohua Tony Hu, Drexel University, USA (xh29@drexel.edu)
==Important Dates==
- Electronic submission of full papers: August 19, 2020
- Notification of paper acceptance: Oct 16, 2020
- Camera-ready of accepted papers: Nov 10, 2020
- Conference: Dec 10-13, 2020
