[ecoop-info] IEEE BigData 2021 Call for Papers

CFP Conference cfp.conference2016 at GMAIL.COM
Sun May 2 04:15:33 CEST 2021

*Call for Papers*

*2021 IEEE International Conference on Big Data  (IEEE BigData 2021)*


December 15-18, 2021,  Orlando, FL, USA

(if the covid-19 pandemic is under control by then, otherwise it will be

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 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 IEEE Big Data 2020 ( http://bigdataieee.org/BigData2020/ ,
regular paper acceptance rate: 15.7%) was held online, Dec 10-13, 2020 with
close to 1100 registered participants from 50 countries

The 2021 IEEE International Conference on Big Data (IEEE BigData 2021) 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 Seattle this year. *Example
topics of interest includes but is not limited to the following*:

1.     Big Data Science and Foundations

a.     Novel Theoretical Models for Big Data

b.     New Computational Models for Big Data

c.     Data and Information Quality for Big Data

d.     New Data Standards

2.     Big Data Infrastructure

a.     Cloud/Grid/Stream Computing for Big Data

b.     High Performance/Parallel Computing  Platforms for Big Data

c.     Autonomic Computing and Cyber-infrastructure, System Architectures,
Design and Deployment

d.     Energy-efficient Computing for Big Data

e.     Programming Models and Environments for Cluster, Cloud, and Grid
Computing to Support Big Data

f.      Software Techniques and Architectures in Cloud/Grid/Stream Computing

g.     Big Data Open Platforms

h.     New Programming Models for Big Data beyond Hadoop/MapReduce, STORM

i.      Software Systems to Support Big Data Computing

3.     Big Data Management

a.     Search and Mining of variety of data including scientific and
engineering, social, sensor/IoT/IoE, and multimedia data

b.     Algorithms and Systems for Big DataSearch

c.     Distributed, and Peer-to-peer Search

d.     Big Data Search  Architectures, Scalability and Efficiency

e.     Data Acquisition, Integration, Cleaning,  and Best Practices

f.      Visualization Analytics for Big Data

g.     Computational Modeling and Data Integration

h.     Large-scale Recommendation Systems and Social Media Systems

i.      Cloud/Grid/Stream Data Mining- Big Velocity Data

j.      Link and Graph Mining

k.     Semantic-based Data Mining and Data Pre-processing

l.      Mobility and Big Data

m.   Multimedia and Multi-structured Data- Big Variety Data

4.     Big Data Search and Mining

a.     Social Web Search and Mining

b.     Web Search

c.     Algorithms and Systems for Big Data Search

d.     Distributed, and Peer-to-peer Search

e.     Big Data Search  Architectures, Scalability and Efficiency

f.      Data Acquisition, Integration, Cleaning,  and Best Practices

g.     Visualization Analytics for Big Data

h.     Computational Modeling and Data Integration

i.      Large-scale Recommendation Systems and Social Media Systems

j.      Cloud/Grid/StreamData Mining- Big Velocity Data

k.     Link and Graph Mining

l.      Semantic-based Data Mining and Data Pre-processing

m.   Mobility and Big Data

n.     Multimedia and Multi-structured Data- Big Variety Data

5.     Big Data Learning and Analytics

a.     Predictive analytics on Big Data

b.     Machine learning algorithms for Big Data

c.     Deep learning for Big Data

d.     Feature representation learning for Big Data

e.     Dimension reduction for Big Data

f.      Physics informed Big Data learning

6.     Ethics, Privacy and Trust in Big Data Systems

a.     Techniques and models for fairness and diversity

b.     Experimental studies of fairness, diversity, accountability, and

c.     Techniques and models for transparency and interpretability

d.     Trade-offs between transparency and privacy

e.     Intrusion Detection for Gigabit Networks

f.      Anomaly and APT Detection in Very Large Scale Systems

g.     High Performance Cryptography

h.     Visualizing Large Scale Security Data

i.      Threat Detection using Big Data Analytics

j.      Privacy Preserving Big Data Collection/Analytics

k.     HCI Challenges for Big Data Security & Privacy

l.      Trust management in IoT and other Big Data Systems

7.     Hardware/OS Acceleration for Big Data

a.     FPGA/CGRA/GPU accelerators for Big Data applications

b.     Operating system support and runtimes for hardware accelerators

c.     Programming models and platforms for accelerators

d.     Domain-specific and heterogeneous architectures

e.     Novel system organizations and designs

f.      Computation in memory/storage/network

g.     Persistent, non-volatile and emerging memory for Big Data

h.     Operating system support for high-performance network architectures

8.     Big Data Applications

a.     Complex Big Data Applications in Science, Engineering, Medicine,
Healthcare, Finance, Business, Law, Education, Transportation, Retailing,

b.     Big Data Analytics in Small Business Enterprises (SMEs),

c.     Big Data Analytics in Government, Public Sector and Society in

d.     Real-life Case Studies of Value Creation through Big Data Analytics

e.     Big Data as a Service

f.      Big Data Industry Standards

g.   Experiences with Big Data Project Deployments


The Industrial Track solicits papers describing implementations of Big Data
solutions relevant to industrial settings. The focus of industry track is
on papers that address the practical, applied, or pragmatic or new research
challenge issues related to the use of Big Data in industry. We accept full
papers (up to 10 pages) and extended abstracts (2-4 pages).

*Student Travel Award*

IEEE Big Data 2021 will offer* student travel *to student authors
(including post-docs)

*Paper Submission:*

Please submit a full-length paper (up to *10 page IEEE 2-column format*)
through the online submission system.


Papers should be formatted to IEEE Computer Society Proceedings Manuscript
Formatting Guidelines (see link to "formatting instructions" below).

*Important Dates:*

Electronic submission of full papers: September 5, 2021

Notification of paper acceptance: Oct 27, 2021

Camera-ready of accepted papers: Nov 15, 2021

Conference: Dec 15-18, 2021


To unsubscribe from the BIGDATA list, click the following link:

More information about the ecoop-info mailing list