Impact Factor and Journal Rank of Big Data

About:

Big Data is the leading peer-reviewed journal addressing the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The journal serves as a vital platform for researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to explore and advance the field of data science.

Objective: The primary objective of Big Data is to foster advancements in the theory, methods, technologies, and applications of big data. The journal covers a wide range of topics including industry standards, new technologies, data acquisition, cleaning, distribution, data protection, privacy policies, business intelligence, visualization principles, physical interfaces, robotics, and social networking advantages. It aims to facilitate collaborations and innovations that enhance operational efficiency, profitability, and communication through effective use of big data.

Interdisciplinary Focus: Big Data adopts an interdisciplinary approach, encompassing contributions from data science, computer science, statistics, business analytics, information systems, and related fields. It encourages research that addresses fundamental challenges and explores emerging trends in big data analytics, infrastructure development, and data-driven decision-making processes. The journal promotes the integration of diverse methodologies and perspectives to advance the understanding and application of big data across various domains.

Global Reach and Impact: With a global readership and contributions from diverse stakeholders, Big Data significantly impacts research and practice in data science worldwide. The journal publishes innovative research, methodologies, and best practices that influence industry standards, academic discourse, and policy development related to big data. Its comprehensive coverage supports the evolution of data-driven strategies and technologies to address contemporary challenges and opportunities.

High Standards and Rigorous Review: Maintaining high academic standards, Big Data employs a rigorous peer-review process. Each submitted manuscript undergoes thorough evaluation by experts in the field to ensure methodological rigor, scientific accuracy, and originality of contributions. This stringent review process upholds the integrity and credibility of the journal, ensuring that only impactful and authoritative research is published.

Significance: Big Data plays a crucial role in advancing knowledge and innovation in the realm of data science and analytics. By providing a platform for cutting-edge research and practical insights, the journal facilitates the development of scalable, secure, and ethical practices in managing and utilizing big data. It serves as a vital resource for stakeholders seeking to harness the transformative potential of big data to drive advancements in research, industry, governance, and societal outcomes.

Journal Home:  Journal Homepage

Editor-in-Chief:  Zoran Obradovic

scope: The journal addresses fundamental questions in the field of data science and supports the efforts of a diverse community including researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers. It aims to enhance operational efficiency, profitability, and communication across various sectors. Here is an overview of its key focus areas and scope:

1. Big Data Industry Standards:
Research on industry standards and best practices in big data management and analytics.
Innovations in data storage, processing frameworks, and scalable computing architectures.

2. New Technologies for Big Data:
Development and evaluation of new technologies tailored for handling big data.
Advancements in distributed computing, cloud computing, and edge computing for big data applications.

3. Data Acquisition, Cleaning, and Distribution:
Methods and strategies for acquiring, cleaning, integrating, and distributing large-scale datasets.
Research on data preprocessing, data transformation, and data quality assessment in big data environments.

4. Data Protection, Privacy, and Policy:
Ethical and regulatory issues related to data protection and privacy in big data applications.
Research on privacy-preserving techniques, data anonymization, and compliance with data protection regulations.

5. Business Interests and Product Development:
Applications of big data analytics in driving business insights and innovation.
Research on data-driven decision-making, predictive analytics, and customer behavior analysis.

6. Evolution of Business Intelligence:
The role of business intelligence tools and platforms in leveraging big data for strategic decision-making.
Research on data visualization, dashboards, and user interfaces for business intelligence.

7. Visualization and Design of Big Data Infrastructures:
Design principles and methodologies for scalable and efficient big data infrastructures.
Innovations in data visualization techniques, visual analytics, and interactive data exploration.

8. Social Networking and Digital Platforms:
Impact of big data on social networking platforms such as Facebook, Twitter, Amazon, Google, and others.
Research on social media analytics, sentiment analysis, recommendation systems, and personalized content delivery.

Print ISSN:  21676461,

Electronic ISSN:  2167647X

Abstracting and Indexing:  Scopus, Science Citation Index Expanded

Imapct Factor 2023:  4.6

Subject Area and Category:   Computer Science , Computer Science Applications , Information Systems , Decision Sciences , Information Systems and Management

Publication Frequency:  

H Index:  41

Best Quartile:

Q1:  Information Systems and Management

Q2:  

Q3:  

Q4:  

Cite Score:  9.1

SNIP:  1.677

Journal Rank(SJR):  0.858

Latest Articles:   Latest Articles in Big Data

Guidelines for Authors: Big Data Author Guidelines

Paper Submissions: Paper Submissions in Big Data

Publisher:  Mary Ann Liebert Inc.

Country:  United States