View source for BIGCOMP 2020

{{Event
|Acronym=BIGCOMP 2020
|Title=2020 IEEE International Conference on Big Data
|Series=BIGCOMP
|Type=Conference
|Field=big data, cloud computing, artificial intelligence, database
|Start date=2020/12/10
|End date=2020/12/13
|Homepage=http://bigdataieee.org/BigData2020/index.html
|City=Atlanta
|State=GA
|Country=USA
|Has host organization=IEEE Computer Society
|Has coordinator=Yubao Wu
|has general chair=Srinivas Aluru, Chengxiang Zhai
|has program chair=Chris Jermaine, Xintao Wu, Li Xiong
|has workshop chair=Eyhab Ai-Masri, Zhiyuan Chen, Jeff Saltz
|has tutorial chair=Rafal A. Angryk, Hui Zhang
}}
'''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
Acronym	BIGCOMP 2020 +
Camera ready due	November 10, 2020 +
End date	December 13, 2020 +
Event in series	BIGCOMP +
Event type	Conference +
Has coordinates	33° 44' 56", -84° 23' 25"Latitude: 33.748991666667 Longitude: -84.390263888889 +
Has coordinator	Yubao Wu +
Has general chair	Srinivas Aluru + and Chengxiang Zhai +
Has location city	Atlanta +
Has location country	Category:USA +
Has location state	GA +
Has program chair	Chris Jermaine +, Xintao Wu + and Li Xiong +
Has tutorial chair	Rafal A. Angryk + and Hui Zhang +
Has workshop chair	Eyhab Ai-Masri +, Zhiyuan Chen + and Jeff Saltz +
Homepage	http://bigdataieee.org/BigData2020/index.html +
IsA	Event +
Notification	October 16, 2020 +
Paper deadline	August 19, 2020 +
Start date	December 10, 2020 +
Submission deadline	August 19, 2020 +
Title	2020 IEEE International Conference on Big Data +