Big Data

Aims & Scope

Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data.

The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions.

Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government.

Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.

View Aims & Scope

---

Metrics & Ranking

SJR (SCImago Journal Rank)

Year	Value
2024	0.692

Quartile

Year	Value
2024	Q2

Impact Factor

Year	Value
2024	2.60

Journal Rank

Year	Value
2024	8220

Journal Citation Indicator

Year	Value
2024	551

---

---

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science
• Medline

---

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.

---

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Data Science and its Relationship to Big Data and Data-Driven Decision Making

  Citation: 1048

  Authors: Foster, Tom

• Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments

  Citation: 1047

  Authors: Alexandra

• The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery

  Citation: 646

  Authors: Melanie

• FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media

  Citation: 634

  Authors: Kai, Deepak, Suhang, Dongwon, Huan

• Deep Learning for Time Series Forecasting: A Survey

  Citation: 560

  Authors: José F., Dalil, Abderrazak, Francisco, Alicia

• Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

  Citation: 441

  Authors: Haneen Arafat, Ahmad B.A., Omar, Ahmad S., Mahmoud Bashir, Hamzeh S., V.B. Surya

• The KIT Motion-Language Dataset

  Citation: 188

  Authors: Matthias, Christian, Tamim

• A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science

  Citation: 172

  Authors: James H., Vipin

• Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors

  Citation: 154

  Authors: Narges, Saul, Ann Marie, Aaron, Somesh, David

• Predictive Modeling With Big Data: <i>Is Bigger Really Better</i>?

  Citation: 150

  Authors: Enric, David, Foster

---