Applications of Artificial Intelligence and Data Management in Data Analysis
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: 31 July 2025 | Viewed by 77
Special Issue Editors
Interests: big data; data analytics; data management; databases; software engineering; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: data mining; machine learning; data analytics; database management; information privacy and security
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; big data management
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) has revolutionized the field of data analysis, enabling organizations to extract valuable insights from large and complex datasets. AI algorithms and techniques can process data more efficiently, identify patterns and trends that humans might miss, and make predictions with greater accuracy.
The focus of this Special Issue is on leveraging AI techniques to enhance data management processes and extract valuable insights. It intends to explore how AI can improve data storage, retrieval, organization, and analysis, ultimately optimizing the use of data within organizations, and also how AI can enhance traditional data analysis methods, automate tasks, and uncover patterns that would be difficult or impossible for humans to identify.
The scope of this Special Issue encompasses a wide range of AI techniques and their applications across various industries. It includes, but is not limited to:
- Machine learning: using algorithms to teach computers to learn from data and make predictions or decisions.
- Natural language processing: analyzing and understanding human language, including text and speech.
- Predictive analytics: forecasting future trends and outcomes based on historical data.
- Data preprocessing and integration: using AI to clean, normalize, and prepare data for analysis and combining data from various sources into a unified dataset.
- Data governance: implementing AI-powered tools for data quality management, compliance, and security.
- Data warehousing and data lakes: utilizing AI to optimize the design, management, and querying of data warehouses and lakes.
- Data visualization: creating interactive and informative visualizations using AI-powered tools.
The primary purpose of applying AI in data management is to improve the efficiency, effectiveness, and value of data analysis. By automating tasks, enhancing data quality, and facilitating data access, AI can help organizations.
The purpose of this Special Issue is to add to the body of literature and to help organizations to:
- Make data-driven decisions: AI can provide insights and recommendations based on data analysis, enabling informed decision-making.
- Increase efficiency and reduce costs: AI can automate manual tasks and optimize data storage and processing, leading to cost savings.
- Improve data quality: AI can help identify and address data quality issues, ensuring data accuracy and reliability.
- Enhance data governance: AI can automate data governance tasks, such as data classification and access control.
The purpose of this Special Issue is also to use AI to discover new opportunities to identify emerging trends and uncover hidden opportunities that might be missed by human analysis.
Prof. Dr. Jorge Bernardino
Prof. Dr. Le Gruenwald
Dr. Elio Masciari
Prof. Dr. Laurent D'Orazio
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence (AI)
- data management
- data analysis
- machine learning
- data governance
- predictive analytics
- data quality
- data visualization
- data mining
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