Symmetry and Asymmetry Phenomena in Incomplete Big Data Analysis

Dear Colleague,

In this era of information explosion, people are inundated with big data. The global data sum is predicted to grow from 33 ZB in 2018 to 175 ZB by 2025. Meanwhile, data are commonly incomplete in many big-data-related applications such as environmental monitoring systems, e-commerce systems, and wireless sensor networks, as the related information or relationships are unlikely to be fully observed or collected in practice. Although some information is missing from incomplete data, they still contain rich latent knowledge and patterns, e.g., users’ potential preferences on items in e-commerce systems. Hence, identifying how to efficiently and effectively filter valuable knowledge and patterns out of incomplete big data has become a significant challenge.

Generally, data from real applications have two kinds of distributions, i.e., symmetric and asymmetric distributions. For example, social networks and protein networks commonly involve a symmetric interactions relationship. On the other hand, traffic data obviously have asymmetric probability distributions between accidents and normal situations. Therefore, it is extremely crucial to consider symmetry and asymmetry phenomena in incomplete big data analysis.  

This Special Issue aims at exploring the latest up-to-date theory, methods, and applications regarding incomplete big data analysis with symmetry and asymmetry phenomena. In particular, new interdisciplinary approaches, open-source tools, and open-source datasets are especially welcome.

Prof. Dr. Xin Luo
Prof. Dr. Di Wu
Guest Editors

Manuscript Submission Information

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