Data Intelligence and Computational Analytics

Dear Colleagues,

Data intelligence refers to the methods and technologies used by individuals or organizations to understand their data, turning raw data into useful information and actionable insights. It goes beyond simply collecting and storing data by focusing on its context, quality, and how data can be used to drive better decisions and outcomes. Computational analytics refers to usages of computational techniques to analyze and interpret datasets (including big data), enabling data-driven decision making and discovery. It involves applying business intelligence, computational, mathematical, and statistical methods to extract information, discover knowledge, and gain insights from data. It often uses tools and techniques from computer science, data science, mathematics, statistics, and related disciplines. This Topic invites submissions on theoretical and practical issues on data intelligence and computational analytics, including but not limited to the following:

• Data science;
• Data management, including metadata management;
• Data quality;
• Data governance;
• Data integration, data fabric, data lake, data mart, data mesh, data vault, data warehouse;
• Data-driven decision making, problem solving;
• Data-driven computational techniques;
• Data analysis and data analytics;
• Graph analytics and text and linguistic analytics;
• Cloud and high-performance computing for data intelligence and computational analytics;
• AI and machine learning, including deep learning;
• Data intelligence and computational analytics applications in business, healthcare, science, engineering, social sciences, or other areas.

This Topic focuses on one theme—namely, data intelligence and computational analytics. However, it provides authors with multiple choices of venues—namely, five different journals. Topic Editors

Prof. Dr. Carson K. Leung
Prof. Dr. Fei Hao
Dr. Xiaokang Zhou
Topic Editors