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Big Data Networks welcomes submissions that report innovative solutions to problems of network and data scale in communication, information, and interaction networks. The scope includes but is not limited to i) computer communication networks; ii) networks for sensing and telemetry; iii) application networks including the web; iv) platforms such as online social media networks; and, more broadly, v) networks representing relationships between entities, e.g., resources and consumers.
Potential contexts include i) network design, operations, and management; ii) measurement of network performance and resource usage; iii) characterization of users, relationships and information flows. Approaches may cover any area of the Big Data lifecycle, including data acquisition, storage and verification; scalable computation systems and methods; Data Science and statistical approaches to knowledge discovery and modeling, decision support and prediction, including machine learning and AI; and, operational systems and tool derived from Big Data analyses. All submissions should explicitly state the challenges for network and data scale that motivates the reported work.
Submissions to Big Data Networks are no longer possible via Frontiers in Digital Humanities, as this field journal is now closed. This section still welcomes submissions via Frontiers in Big Data.
Indexed in: Google Scholar, DOAJ, CrossRef, CLOCKSS, OpenAIRE
Big Data Networks welcomes submissions of the following article types: Brief Research Report, Clinical Trial, Community Case Study, Conceptual Analysis, Core Concept, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, New Discovery, Opinion, Original Research, Perspective, Policy and Practice Reviews, Review, Specialty Grand Challenge, Study Protocol, Systematic Review and Technology and Code.
All manuscripts must be submitted directly to the section Big Data Networks, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section Big Data Networks will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in all Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Big Data.
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