Journal of Big Data [electronic resource] /edited by Borko Furht, Taghi M. Khoshgoftaar.
Contributor(s): Furht, Borko [editor.] | Khoshgoftaar, Taghi M [editor.] | SpringerLink (Online service).
Material type:
Continuing resourceAnalytics: Show analyticsPublisher: Cham : Springer International Publishing : Imprint: Springer. Description: online resource.ISSN: 2196-1115.Subject(s): Database management | Data mining | Information storage and retrieval | Computer science -- Mathematics | Computer mathematics | Electrical engineering | Database Management | Information Storage and Retrieval | Data Mining and Knowledge Discovery | Computational Science and Engineering | Mathematical Applications in Computer Science | Communications Engineering, NetworksOnline resources: Open Access Summary: The 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 a seminal source of innovative material.
The 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 a seminal source of innovative material.
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