Big Data, a highly innovative, open access peer-reviewed journal, provides a unique forum for world-class research exploring the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data, including data science, big data infrastructure and analytics, and pervasive computing.
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
Big Data is under the editorial leadership of Editor-in-Chief Vasant Dhar, PhD, Stern School of Business, New York University and Executive Editor Eugene Kolker. View the entire editorial board.
To ensure the broadest and most timely dissemination of information, Big Data publishes all original peer-reviewed research articles as open access. Access to the Journal's premium content, in print and online, is available through subscription access and includes value-added editorials, round-table discussions, industry coverage, review articles, and archival content as developed.
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Audience: Data miners, computer scientists, statisticians, researchers, chief data scientists, database engineers, software engineers, web developers, policy makers, CEOs of data startups, professors of computer science, mathematics, physics, statistics, and computational biology, among others.
The views, opinions, findings, conclusions and recommendations set forth in any
Journal article are solely those of the authors of those articles and do not necessarily
reflect the views, policy or position of the Journal, its Publisher, its editorial
staff or any affiliated Societies and should not be attributed to any of them.