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Business & Management | Annals of Data Science

Annals of Data Science

Annals of Data Science

Editor-in-Chief: Yong Shi

ISSN: 2198-5804 (print version)
ISSN: 2198-5812 (electronic version)

Journal no. 40745

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  • Publishes a broad range of cutting-edge research findings, experimental results and case studies of data science  
  • Focuses on semi-structured and unstructured data analysis, Big Data modeling, Big Data mining, knowledge representation of Big Data  
  • It is an official journal of International Academy of Information Technology and Quantitative Management (IAITQM)  
Annals of Data Science (AODS) is an academic journal focusing on Big Data analytics and applications. It not only promotes how to use interdisciplinary techniques, including statistics, artificial intelligence and optimization, to process Big Data and conduct data mining, but also how to use the knowledge gleaned from Big Data for real-life applications. AODS accepts high-quality contributions on the foundations of data science, technical papers on various challenging problems in Big Data and meaningful case studies concerning business analytics in the context of Big Data.

Related subjects » Artificial Intelligence - Business & Management - Statistics

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Google Scholar, EBSCO Discovery Service, INIS Atomindex, OCLC WorldCat Discovery Service, ProQuest ABI/INFORM, ProQuest Advanced Technologies & Aerospace Database, ProQuest Business Premium Collection, ProQuest Central, ProQuest SciTech Premium Collection, ProQuest Technology Collection, ProQuest-ExLibris Primo, ProQuest-ExLibris Summon

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  • Aims and Scope

    Aims and Scope


    Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.    
    ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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