Advanced Data Analytics for Large-scale Complex Data Environments

Submission Deadline: 30 August 2017

IEEE Access invites manuscript submissions in the area of Advanced Data Analytics for Large-scale Complex Data Environments.

Big Data is defined as an emerging paradigm that includes complex and large-scale information beyond the processing capability of conventional tools. Traditional data analytics methods have been commonly used for many applications, such as text classification, image recognition, and video tracking. For analysis purposes, these data often need to be represented as vectors. However, many other types of data objects in real-world applications contain rich feature vectors and structure information, such as chemical compounds in bio-pharmacy, brain regions in brain networks and users in social networks. Unfortunately, vector representations are very simple features that do not inherently contain the object’s structure information. In reality, objects may have complicated characteristics depending on how the objects are assessed and characterized. Data may also reside in heterogeneous domains, such as traditional tabular-based data, sequential patterns, social networks, time series information, and semi-structured data. As a result, novel data analytics methods are desired to discover meaningful knowledge in advanced applications from objects with large-scale complex characteristics.

This Special Section in IEEE Access expects to solicit contributions for advanced data analytics for complex big data environments. The topics of interest include, but are not limited to:

  • Supervised/Unsupervised/Semi-supervised Learning
  • Semi-structured learning
  • Big graph/Network analysis
  • Streaming data processing
  • Online learning for big data
  • Deep learning for big data
  • Social networks for big data
  • Time series learning
  • Heterogeneous transfer learning for big data
  • Healthcare big data analytics
  • Medical big data analytics
  • Big data in financial services
  • Big data analytics for social media
  • Deep image processing (e.g. scene analysis, super-resolution, denoising, and image fusion)
  • Classification and target detection form high dimension images
  • Large-scale biometric recognition (e.g. face recognition and person re-identification)
  • Stochastic learning
  • Parallel and distributed machine learning algorithms
  • High performance computing implementations for large-scale point matching and alignment
  • High performance computing implementations for high dimension image processing

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor: Jia Wu, University of Technology Sydney, Australia

Guest Editors:

  1. Shirui Pan, University of Technology Sydney, Australia
  2. Junjun Jiang, National Institute of Informatics, Tokyo
  3. Zhihua Cai, China University of Geosciences (Wuhan), China
  4. Bo Du, Wuhan University, China
  5. Yingjie Tian, Chinese Academy of Sciences, China
  6. Shuaiqiang Wang, The University of Manchester, UK
  7. Haishuai Wang, Washington University in St. Louis, USA


Related IEEE Access Special Sections:

  1. Big Data Analytics in Internet-of-Things and Cyber-Physical System
  2. Complex System Health Management Based on Condition Monitoring and Test Data
  3. Heterogeneous Crowdsourced Data Analytics


IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: Bora M. Onat, Managing Editor, IEEE Access (Phone: (732) 562-6036,