Call for Papers
2023 IEEE International Conference on Big Data (IEEE BigData 2023)
http://bigdataieee.org/BigData2023/
December 15-18, 2023 – Sorrento, Italy
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 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 IEEE Big Data 2021 (http://bigdataieee.org/BigData2021/ , regular paper acceptance rate: 19.9%) was
held online, Dec 15-18, 2021 with close to 1089 registered participants from 52 countries
* The IEEE Big Data 2022 (http://bigdataieee.org/BigData2022/ , regular paper acceptance rate: 19.2%) was
held in Osaka, Japan, Dec 17-20, 2022 with close to 1250 registered participants from 54 countries.
The 2023 IEEE International Conference on Big Data (IEEE BigData 2023) 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 Sorrento Italy
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. Data Acquisition, Integration, Cleaning, and Best Practices
b. Computational Modeling and Data Integration
c. Large-scale Recommendation Systems and Social Media Systems
d. Cloud/Grid/Stream Data Mining- Big Velocity Data
e. Mobility and Big Data
f. Multimedia and Multi-structured Data- Big Variety Data
g. Compliance and Governance for Big 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. Link and Graph Mining
g. Semantic-based Data Mining and Data Pre-processing
h. Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE,
and multimedia 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
g. Visualization Analytics for Big Data
h. Big data management for pre-training
i. Big data management for fine-tuning
j. Big data management for promt-tuning
k. Prompt Engineering and its Management
l. Foundation Model Operationalization for multiple users
6. Data Ecosystem
a. Data ecosystem concepts, theory, structure, and process
b. Ecosystem services and management
c. Methods for data exchange, monetization, and pricing
d. Trust, resilience, privacy, and security issues
e. Privacy preserving Big Data collection/analytics
f. Trust management in Big Data systems
g. Ecosystem assessment, valuation, and sustainability
h. Experimental studies of fairness, diversity, accountability, and transparency
7. Big Data Applications
a. Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law,
Education, Transportation, Retailing, Telecommunication
b. Big Data Analytics in Small Business Enterprises (SMEs),
c. Big Data Analytics in Government, Public Sector and Society in General
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
Vision Papers
The Vision papers category includes papers that describe emerging areas of research, or new applications,
related to Big Data. Vision papers do not need to include theoretical, or experimental results, but should
describe future research in an important new area or technical problem, and explain the challenges,
opportunities and impact of such research. Vision papers should include "[Vision Paper]" in their title,
and should be up to 5 pages including references.
INDUSTRIAL & Government Track
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).
The Government Track welcomes papers discussing the usefulness and need for publicly-contribution
big data and open data and their use. Specifically, data utilization scenarios, needs analysis,
data utilization obstacle analysis and solutions, data integration processes, interfaces as data
utilization solutions, visualization, use cases, evidence-based policy making, building an ecosystem
for solving social issues, analyzing their cases, comparing international and regional differences,
and conducting comparative surveys before and after specific events (like Covid-19). We are also
looking for other big data solutions related to national and local governments, and public services.
Please submit an extended abstract (2-4 pages) OR a full-length paper (up to 10 pages) through the
online submission page (Industrial & Government Track dedicated page)
Paper Submission:
Please submit a full-length paper (up to 10 page IEEE 2-column format, reference pages counted in
the 10 pages ) through the online submission system.
https://wi-lab.com/cyberchair/2023/bigdata23/index.php
Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines
(see link to "formatting instructions" below).
https://www.ieee.org/conferences/publishing/templates.html
Important Dates:
Electronic submission of full papers: Sept 3, 2023
Notification of paper acceptance: Oct 27, 2023
Camera-ready of accepted papers: Nov 17, 2023
Conference: Dec 15-18, 2023
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