Submission Deadline: 30 June 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:
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Associate Editor: Jia Wu, University of Technology Sydney, Australia
Related IEEE Access Special Sections:
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, firstname.lastname@example.org)