Big Data Analytics is a multi-disciplinary open access, peer-reviewed journal, which welcomes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of big data science analytics. Spanning the life sciences, social sciences, engineering, physical and mathematical sciences, Big Data Analytics aims to provide a platform for the dissemination of research, current practices, and future trends in the emerging discipline of big data analytics.
Associative classification aims at building accurate and interpretable classifiers by means of association rules. A major problem in this field is that existing proposals do not scale well for Big Data. This work proposes adaptations of common associative classification algorithms for different Big Data platforms.
This timely special issue serves as an interdisciplinary forum for researchers across the world to share their findings in this exciting area. We solicit original contributions on machine learning theories, deep learning models, surveys of recent advances, benchmark datasets and novel application scenarios. Click the link above to see more.
Aims and scope
Big Data Analytics is pleased to announce a call for papers for a new article collection of original, unpublished, and novel in-depth research that makes significant methodological or application contributions to the field of visualization, interpretation and descriptive big data science. Read more and all the current calls for papers at the link above.
"As part of its vision, the journal will underline the importance of adopting a flexible and open-ended definition of big data"
In this Q&A, Editor-in-Chief Amir Hussain tells us more about Big Data Analytics.
Amir Hussain, Editor-in-Chief
Amir Hussain obtained his BEng and PhD from the University of Strathclyde in Glasgow. Following a Research Fellowship at the University of Paisley and a research Lectureship at the University of Dundee, he joined the University of Stirling in 2000, where he is currently Professor of Computing Science and founding Director of the Cognitive Big Data Informatics (CogBID) Laboratory. His research interests are multi-disciplinary with a focus on brain-inspired, multi-modal cognitive big data technology for solving complex real-world problems. He has authored over 300 publications (including over a dozen books and over 100 journal papers), conducted and led collaborative research with industry, partnered in major European and international research programs, and supervised more than 30 PhDs.
In addition to his role as Editor-in-Chief of Big Data Analytics, Amir is also founding Chief Editor of the Springer journal Cognitive Computation, and Associate Editor for a number of other leading journals. He has served as an invited speaker and organizing committee co-chair for over 50 top international conferences and workshops. He has served as the founding Publications Co-Chair of the INNS Big Data section and the annual INNS Conference on Big Data, and Chapter Chair of the IEEE UK and RI’s Industry Applications Society. He is a Fellow of the UK’s Higher Education Academy and Senior Fellow of the Brain Sciences Foundation.
Annual journal metrics
122.2 days to first decision for reviewed manuscripts only
106.9 days to first decision for all manuscripts
278.5 days from submission to acceptance
25 days from acceptance to publication
8 Altmetric mentions