AI-Based Data Science and Database Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 December 2024 | Viewed by 863
Special Issue Editors
Interests: database; big data; data mining; artificial intelligence
Special Issue Information
Dear Colleagues,
As machine learning (ML), deep learning (DL), and large language models (LLMs) become widely adopted across various applications and disciplines, the synergy between database (DB) systems and the artificial intelligence (AI) community is becoming increasingly evident. AI technology, with its unparalleled modeling and generalization capabilities, is at the forefront of technological advancement, catalyzing further development in numerous fields. Beyond the contributions of algorithms and models themselves, the quality of training data significantly impacts the performance of AI models. Accurate, consistent, and representative clean datasets are crucial for enhancing the modeling effectiveness and generalization capability of AI models. The steps involved in data preparation, cleaning, and management, which greatly influence data quality, are closely linked to research within the database community. Additionally, the ML pipeline also depends on mechanisms for storing and querying ML artifacts. Conversely, the database field can also benefit from AI research. Traditional methods in the database domain, which often rely on constraint- or rule-based approaches, can leverage AI to reduce the heavy dependence on human supervision and offer new perspectives and solutions for addressing traditional complex problems.
This Special Issue focuses on exploring the potential at the intersection of the database and AI fields, emphasizing research that combines the strengths of both domains. By harnessing the mutual empowerment of these fields, we aim to advance the progress of both database and AI technologies.
The Special Issue is particularly interested in topics such as, but not limited to, the following:
- Advanced data cleaning techniques for AI applications;
- Seamless data integration solutions for AI-driven processes;
- Comprehensive data discovery methods for AI development;
- Lifecycle management of datasets in AI pipelines;
- Automated data preprocessing for AI;
- AI-driven techniques for database schema design and optimization;
- Enhanced AI-based functionality within modern DBMS;
- AI-based data discovery and profiling;
- Integrated AI-based data cleaning and data integration solutions;
- AI-powered data analytics and exploration in data lakes.
Dr. Yu Sun
Dr. Chengliang Chai
Guest Editors
Manuscript Submission Information
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Keywords
- data management in AI model lifecycle
- AI-based functionality inside DBMS
- AI-based data science
- AI-based data discovery
- AI-based data preparation
- AI-based database systems
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