Call for papers

Scalable, Intelligent Data Analytics and Learning

Guest Editors:

Kaizhu Huang
Xi’an Jiaotong-Liverpool University, China
Jun Sun
Fujitsu Research & Development Center, China
Zenglin Xu
University of Electronic Science and Technology of China, China
Amir Hussain
Stirling University, UK 

With the fast evolving technology for data collection, data transmission, and data analysis, the scientific, biomedical, and engineering research communities are undergoing a profound transformation where discoveries and innovations increasingly rely on massive amounts of data. New prediction techniques, including novel statistical, mathematical, and modeling techniques are enabling a paradigm shift in scientific and biomedical investigation. Data become the fourth pillar of science and engineering, offering complementary insights in addition to theory, experiments, and computer simulation. Advances in machine learning, data mining, and visualization are enabling new ways of extracting useful information from massive data sets. The characteristics of volume, velocity, variety and veracity bring challenges to current data analytics techniques. It is desirable to scale up data analytics techniques for modeling and analyzing big data from various domains.

The goal of the special issue is to invite original contributions reporting the latest advances and hence to provide professionals, researchers, and technologists with a single forum where they can share the state-of-the-art theories and applications of scalable, intelligent data analytics and learning technologies.

Topics of interest include, but not limited to, the following aspects:

  • Distributed data analytics architectures
  • Theory and algorithms for scalable descriptive statistical modeling
  • Theory and algorithms of scalable predictive statistical modeling
  • Scalable analytics techniques for temporal and spatial data
  • Scalable data analytics algorithms in large graphs
  • Novel applications of scalable machine learning in big data
  • Other related learning algorithm and approaches

Authors intending to submit are encouraged to send a pre-submission enquiry to the Guest editors (kaizhu.huang@xjtlu.edu.cn) to discuss relevance of their work. Authors should indicate in the covering letter that the submitted manuscript is to be considered for the Scalable, Intelligent Data Analytics and Learning (SIDAL) special issue. The manuscripts submitted to SIDAL special issue will be co-edited by the Guest editors and the Big Data Analytics Editorial team.

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