AI-Driven Big Data Processing: Theory, Methodology, and Applications

Submission Deadline: 31 March 2019

IEEE Access invites manuscript submissions in the area of AI-Driven Big Data Processing: Theory, Methodology, and Applications.

With the rapid development of network infrastructures and personal electronic devices, big data generated from Internet, sensing networks, and other equipment are rapidly growing, and have received increased attention in recent years. Big data consists of multisource content, for example, images, videos, audio, text, spatio-temporal data, and wireless communication data. Moreover, big data processing includes computer vision, natural language processing (NLP), social computing, speech recognition, data analysis in Internet of Vehicle (IoV), real-time data analysis in Internet of Things (IoT), and wireless big data processing.

Recently, artificial intelligence (AI)-driven big data processing technologies based on pattern recognition, machine learning, and deep learning, are intensively applied to deal with the large-scale heterogeneous data. However, challenges still exist in the development of AI-driven big data processing.

In computer vision and image processing, increasingly more databases and data streams have been transmitted and collected. One of the biggest challenges in the massive image/video data analysis is to develop energy efficient and real-time methods to extract useful information out of the colossal amount of data being generated every second. In speech signal processing, benefitting from the help of ‘Big Data’ and new AI technology, a lot of progress has also been made in speech processing area. How to build a condition robustness speech processing system using limited labeled data is still a direction to emphasize studying in the future.

In NLP, knowledge is an essential part of artificial intelligence. Many NLP tasks, such as opinion mining, question answering system, and dialog system need big data to get more knowledge to improve the system performance. How to effectively use the existing huge knowledge in NLP systems is still a hot research topic. In wireless communications, facing 5G and beyond systems with the increased antenna number, huge bandwidth and versatile application scenarios, the channel characteristics become more complex and hidden in big volume of data. Simultaneously, there will be a continuous increase in the wireless channel dimension which is already considerably large. In this Special Section in IEEE Access, we invite researchers to discuss the aforementioned challenges; analyzing and processing big data in a more effective and cost reducing way, discovering and understanding knowledge from the data, and generalizing and transferring the discovery into other application fields, are challenging problems to solve.

The topics of interest include, but are not limited to:

  • Foundations of machine learning and pattern recognition
  • Neural networks and deep learning
  • Image big data and computer vision
  • Natural language processing
  • Wireless big data processing
  • Speech big data analysis and speaker recognition
  • Platforms and systems, e.g., architecture design, hardware implementation

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


Associate Editor:  Zhanyu Ma, Beijing University of Posts and Telecommunications, China

Guest Editors:

  1. Sunwoo Kim, Hanyang University, Korea
  2. Pascual Martínez-Gómez, Amazon, US
  3. Jalil Taghia, Uppsala University, Sweden
  4. Yi-Zhe Song, Queen Mary University of London, UK
  5. Huiji Gao, LinkedIn, US


Relevant IEEE Access Special Sections:

  1. Big Data Learning and Discovery
  2. Multimedia Analysis for Internet-of-Things
  3. Advanced Data Analytics for Large-scale Complex Data Environments

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: