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Data Mining and Management welcomes submissions on a wide range of topics, such as intelligent data management, information retrieval, privacy-preserving data sharing and mining, data visual analytics, evaluation and validation, trust and privacy, cybersecurity in social data, and ethics issues with data mining and management.
Data is pervasive, big, diverse, and evolving. Data is still, however, a new type of raw material which requires ingenious and efficient algorithms to turn it into useful knowledge. Data mining is a relatively new way of turning data to knowledge. New types of data demand novel data management research to efficiently store, curate, retrieve, integrate, analyze and understand. Social data and streaming data are two exemplary members in the family of big data. New challenges arise and abound. Classic algorithms and traditional methodologies may require innovative retooling or refinement, and novel algorithms are sought for unprecedented problems due to big data. One prominent issue with social data is, for example, privacy preservation in both data mining and data management.
The section is particularly interested in papers on intelligent data management, information retrieval, search and recommendation, privacy-preserving data sharing and mining, multi-source data fusion, data visual analytics, data-driven hypothesis generation, evaluation and validation, trust and privacy, cybersecurity in social data, data preprocessing (feature selection, discretization or imputation, instance selection), scalable data mining, streamlining algorithms, and ethics issues with data mining and management. High quality papers that go beyond these topics are equally welcome.
Data Mining and Management recognizes the integral nature of the two generally separated areas in the age of big data – effective data mining requires efficient data management, in the meantime, informs and inspires novel data management development. This section squarely serves both research and application needs. The distinctive senior reviewer board and the section’s conducive ambience toward IT applications clearly encourage timely exchange of fast-paced research and development. The section publishes high quality papers and supports authors with a streamlined interactive peer-review system. As an umbrella journal, Frontiers in Big Data can significantly increase the visibility and readership base of articles and authors. Articles that are part of a Research Topic can be cross-listed into other relevant sections and even journals.
Indexed in: Google Scholar, DOAJ, CrossRef, CLOCKSS, OpenAIRE
Data Mining and Management welcomes submissions of the following article types: Brief Research Report, Clinical Trial, Community Case Study, Conceptual Analysis, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, 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 Data Mining and Management, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section Data Mining and Management 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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