AI-Based Data Science and Database Systems

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

Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• 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