Guest Editors
Dr. Wentao Li, Southwest University, China.
Dr. Chao Zhang, Shanxi University, China.
Dr. Tao Zhan, Southwest University, China.
Dr. Hanyu E, University of Alberta, Canada.
Summary
Traditional data analysis usually focuses on employing intelligent methods to analyze and discover useful data hidden in a batch of raw datasets, so as to maximize the value of data. This plays a vital role in making developmental plans for a country, understanding commercial values for organizations, forecasting social demands for individuals, etc. Big data analysis is a special kind of data analysis with more massive volumes of data, which is becoming a driving force for changes in almost all walks of life. Therefore, plenty of traditional methods in data analysis still work in big data analysis, and cognitive granular computing methods are among the representative ones.
As a newly emerged computing paradigm in the field of artificial intelligence, granular computing methods primarily address complicated problems via formulating, processing and communicating information granules for enhancing the validity and efficiency of problem-solving procedures. Since the birth of granular computing, numerous cognitive-systems-inspired tools have been developed under the umbrella of granular computing in both theoretical and application areas, such as three-way decisions that formulate the natural thinking mode of human, multi-granularity structures that appear in complex social networks, cloud models that depict linguistic expressions, etc. During the past decade, big data analysis has become a focal point of scholars and practitioners, and the study on new methods in the context of big data analysis is conducive to understanding and digging massive values from the facet of countries, industries, organizations and individuals. Therefore, exploring new cognitive granular computing methods for big data analysis owns big scientific advances and significant application values.
The goal of this special issue is to collect recent developments in the area of cognitive granular computing methods for big data analysis and how can be applied to real-world issues. Original research work, significantly extended versions of conference papers, and review papers are welcome. Topics of interest include, but are not limited to, the following:
1. Cognitive granular computing methods for the creation, sharing and reuse of big data;
2. Cognitive granular computing tools and technologies for deploying and managing big data;
3. Cognitive granular computing methods to ensure the security and privacy of big data;
4. Cognitive granular computing methods to integrate multiple research methods on big data;
5. New big data analysis techniques by using cognitive granular computing methods;
6. New cognitive granular computing methods emerged from recent big data innovations.
Keywords
• Big data modeling and analysis
• Cognitive granular computing
• The security and privacy of big data
• Uncertainty information analysis in engineering
• Intelligent decision-making techniques
• Engineering data-driven cognitive computing
Published Papers
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