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Introduction
We are now in an era of big data, as large volumes of data with various types are continuously generated every day from all kinds of sectors. Data have become the basis for the running of the modern society, as more and more plans, decisions, and predictions are made based on data. Data analysis is the most widely-employed way to discover the rules, correlations and laws hidden in data. Thus, it constantly attracts a lot of attention from both academia and industries to develop better methods for more effective or efficient data analysis.
But it is worth noting that many unseen challenges have arisen, due to the following several reasons. First, the volume of data is constantly increasing, as more sources are producing data, such as ubiquitous sensors, new mobile devices, and online websites. Second, the type of data is more diverse, such as text data, multimedia data, image data, video data, streaming data, and high-dimensional data. Third, the correlation among data is more complex, and the typical examples are the graph data from social networks, and streaming data from stock markets. Such new challenges are urgently calling for advanced methods of data analysis for a better understanding of what facts those data can tell us and what rules can teach us.
The goal of this article collection is to publish high-quality research related to AI-Empowered methods in data analysis. Research focusing on theoretical and practical issues in the mentioned areas are all welcome. We accept original research papers as well as review articles. We will also consider ‘opinion pieces’ papers where content is dependent on the author's opinion and interpretation. This should still include referencing and citations, but the paper will include views, opinions, and discuss ideas based on perspectives (which will often be expert led).
Contributions are invited on, but not restricted to, the following themes:
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AI-Empowered data pre-processing methods
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AI-Empowered data cleaning methods
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AI-Empowered data mining methods
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AI-Empowered data dimension reduction methods
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AI-Empowered data clustering methods
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AI-Empowered data classification methods
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AI-Empowered regression methods
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AI-Empowered data visualization methods
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AI-Empowered statistical tools for data analysis
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AI-Empowered Bayesian methods for data analysis
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AI-Empowered non-parametric methods for data analysis
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AI-Empowered neural network-based methods for data analysis
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AI-Empowered data governing methods
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AI-Empowered methods for high-dimensional data analysis
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AI-Empowered methods for streaming data analysis
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AI-Empowered methods for multimedia data analysis
Submissions Information
Submissions are made using ScholarOne Manuscripts. Registration and access are available by clicking the button below. Author guidelines must be strictly followed.
For inquiries we invite you to email the editors via Prof. Gao at: [email protected]
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