Aims & scope
Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Academic researchers and practitioners will find the Journal of Big Data to be the definitive, most comprehensive reference in the field.
Open Thematic Series
SpringerOpen is Springer’s new suite of open access journals which will cover all disciplines. SpringerOpen journals are fully and immediately open access and will publish articles under the Creative Commons Attribution license. This makes it easy for authors to fully comply with open access mandates and retain copyright. SpringerOpen journals combine open access and our expertise in delivering high-quality and rapid publications, from online submission systems and in-depth peer review to an efficient, author-friendly production process.
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