IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering (TKDE) is an archival journal published monthly designed to inform researchers, developers, managers, strategic planners, users, and others interested in state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area. Read the full scope of TKDE


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From the December 2018 issue

Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-Grained Air Quality

By Zhongang Qi, Tianchun Wang, Guojie Song, Weisong Hu, Xi Li, and Zhongfei Zhang

Featured Article The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep learning network. The proposed approach utilizes the information pertaining to the unlabeled spatio-temporal data to improve the performance of the interpolation and the prediction, and performs feature selection and association analysis to reveal the main relevant features to the variation of the air quality. We evaluate our approach with extensive experiments based on real data sources obtained in Beijing, China. Experiments show that DAL is superior to the peer models from the recent literature when solving the topics of interpolation, prediction, and feature analysis of fine-gained air quality.

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Editorials and Announcements

Announcements

  • TKDE now offers authors access to Code Ocean. Code Ocean is a cloud-based executable research platform that allows authors to share their algorithms in an effort to make the world’s scientific code more open and reproducible. Learn more or sign up for free.
  • We are pleased to announce that Xuemin Lin, a Scientia Professor in the School of Computer Science and Engineering at the University of New South Wales, Australia, has been named the new Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering starting in 2017.

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