• IEEE.org
  • IEEE CS Standards
  • Career Center
  • About Us
  • Subscribe to Newsletter

0

IEEE
CS Logo
  • MEMBERSHIP
  • CONFERENCES
  • PUBLICATIONS
  • EDUCATION & CAREER
  • VOLUNTEER
  • ABOUT
  • Join Us
CS Logo

0

IEEE Computer Society Logo
Sign up for our newsletter
IEEE COMPUTER SOCIETY
About UsBoard of GovernorsNewslettersPress RoomIEEE Support CenterContact Us
COMPUTING RESOURCES
Career CenterCourses & CertificationsWebinarsPodcastsTech NewsMembership
BUSINESS SOLUTIONS
Corporate PartnershipsConference Sponsorships & ExhibitsAdvertisingRecruitingDigital Library Institutional Subscriptions
DIGITAL LIBRARY
MagazinesJournalsConference ProceedingsVideo LibraryLibrarian Resources
COMMUNITY RESOURCES
GovernanceConference OrganizersAuthorsChaptersCommunities
POLICIES
PrivacyAccessibility StatementIEEE Nondiscrimination PolicyIEEE Ethics ReportingXML Sitemap

Copyright 2025 IEEE - All rights reserved. A public charity, IEEE is the world’s largest technical professional organization dedicated to advancing technology for the benefit of humanity.

  • Home
  • /Digital Library
  • /Journals
  • /Bd
  • Home
  • / ...
  • /Journals
  • /Bd

Call for Papers: IEEE Transactions on Big Data

IEEE Transactions on Big Data seeks submissions for upcoming issues.

About IEEE Transactions on Big Data

The IEEE Transactions on Big Data publishes peer reviewed articles with big data as the main focus. The articles will provide cross disciplinary innovative research ideas and applications results for big data including novel theory, algorithms and applications.


Scope of Interest

Submissions are welcomed on any topic in computer architecture, especially but not limited to: microprocessor and multiprocessor systems, microarchitecture and ILP processors, workload characterization, performance evaluation and simulation techniques, compiler-hardware and operating system-hardware interactions, interconnect architectures, memory and cache systems, power and thermal issues at the architecture level, I/O architectures and techniques, independent validation of previously published results, analysis of unsuccessful techniques, domain-specific processor architectures (e.g., embedded, graphics, network, etc.), real-time and high-availability architectures, reconfigurable systems.

Research areas for big data include, but are not restricted to:

  • big data analytics
  • big data visualization
  • big data curation and management
  • big data semantics
  • big data infrastructure
  • big data standards
  • big data performance analyses
  • intelligence from big data
  • scientific discovery from big data security
  • privacy
  • legal issues specific to big data

Applications of big data in the fields of endeavor where massive data is generated are of particular interest.


Submission Instructions:

This journal is a hybrid publication, allowing either traditional manuscript submission or author-paid Open Access (OA) manuscript submission. Read about OA submissions here.

TBD manuscript types and submission length guidelines are described below. All page limits include references and author biographies. For regular papers, pages in excess of these limits after final layout of the accepted manuscript is complete are subject to Mandatory Overlength Page Charges (MOPC). Note: All supplemental material must be submitted as separate files and must not be included within the same PDF file as the main paper submission. There is no page limit on supplemental files.

  • Regular paper – 12 double column pages (Submissions may be up to 18 pages in length, subject to MOPC. All regular paper page limits include references and author biographies.)
  • Short paper – 8 double column pages
  • Comments paper – 2 double column pages
  • Survey papers – 20 double column pages

A double column page is defined as a 7.875″ x 10.75″ page with 9.5-point type and 11.5-point vertical spacing. These length limits are taking into account reasonably-sized figures and references.

Please use the IEEE Template Selector to find the correct template.

In addition to submitting your paper to IEEE Transactions on Big Data, you are also encouraged to upload the data related to your paper to IEEE DataPort. IEEE DataPort is IEEE's data platform that supports the storage and publishing of datasets while also providing access to thousands of research datasets. Uploading your dataset to IEEE DataPort will strengthen your paper and will support research reproducibility. Your paper and the dataset can be linked, providing a good opportunity for you to increase the number of citations you receive. Data can be uploaded to IEEE DataPort prior to submitting your paper or concurrent with the paper submission. Thank you!

For author information and guidelines on submission criteria, please visit IEEE Transactions on Big Data Information page.


Questions?

Contact:

Editorial board, steering committee, and staff

TBD editorials and guest editorials

LATEST NEWS
2026: 80th Anniversary
2026: 80th Anniversary
The Cybersecurity & AI Junior School Workshop: Bridging the Digital Skills Gap for Future Innovators
The Cybersecurity & AI Junior School Workshop: Bridging the Digital Skills Gap for Future Innovators
Supply Chain Concepts in Health Information Management: Strategic Integration and Information Flow Optimization
Supply Chain Concepts in Health Information Management: Strategic Integration and Information Flow Optimization
The Road Ahead: Preparing for 2030’s Digital Oil & Gas
The Road Ahead: Preparing for 2030’s Digital Oil & Gas
Celebrating Innovation at TechX Florida 2025
Celebrating Innovation at TechX Florida 2025
Read Next

2026: 80th Anniversary

The Cybersecurity & AI Junior School Workshop: Bridging the Digital Skills Gap for Future Innovators

Supply Chain Concepts in Health Information Management: Strategic Integration and Information Flow Optimization

The Road Ahead: Preparing for 2030’s Digital Oil & Gas

Celebrating Innovation at TechX Florida 2025

Quantum Insider Session Series: Practical Instructions for Building Your Organization’s Quantum Team

Beyond Benchmarks: How Ecosystems Now Define Leading LLM Families

From Legacy to Cloud-Native: Engineering for Reliability at Scale

FacebookTwitterLinkedInInstagramYoutube
Get the latest news and technology trends for computing professionals with ComputingEdge
Sign up for our newsletter