IEEE Transactions on Big Data
Published by IEEE
eISSN : 2332-7790
Abbreviation : IEEE Trans. Big Data
Aims & Scope
The IEEE Transactions on Big Data publishes peer reviewed articles with big data as the main focus.
The articles provide cross disciplinary innovative research ideas and applications results for big data including novel theory, algorithms and applications.
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 and legal issues specific to big data.
Applications of big data in the fields of endeavor where massive data is generated are of particular interest. The articles in this journal are peer reviewed in accordance with the requirements set forth in the IEEE PSPB Operations Manual (sections 8.2.1.C & 8.2.2.A).
Each published article was reviewed by a minimum of two independent reviewers using a single-blind peer review process, where the identities of the reviewers are not known to the authors, but the reviewers know the identities of the authors.
Articles will be screened for plagiarism before acceptance.
Corresponding authors from low-income countries are eligible for waived or reduced open access APCs.
View Aims & ScopeMetrics & Ranking
Impact Factor
| Year | Value |
|---|---|
| 2025 | 5.7 |
Journal Rank
| Year | Value |
|---|---|
| 2024 | 2200 |
Journal Citation Indicator
| Year | Value |
|---|---|
| 2024 | 2406 |
SJR (SCImago Journal Rank)
| Year | Value |
|---|---|
| 2024 | 1.571 |
Quartile
| Year | Value |
|---|---|
| 2024 | Q1 |
h-index
| Year | Value |
|---|---|
| 2024 | 34 |
Impact Factor Trend
Abstracting & Indexing
Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.
Subjects & Keywords
Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Decision Sciences, designed to support cutting-edge academic discovery.