DATAI Workshop Schedule 2025 – September 5, 2025
| Time | Type | Topic | Author |
|---|---|---|---|
| 13:30–13:35 | Opening | Opening Remarks: Welcome and Introduction to DATAI Workshop 2025 | Workshop Chairs |
| 13:35–14:05 | Keynote | Data-centric Responsible AI from General ML to LLMs | Steven Euijong Whang |
| 14:05–14:35 | Keynote | Navigating Disruption: The Impact of AI Technologies on Data Integration Research | Ziawasch Abedjan |
| 14:35–14:45 | Break | Coffee Break | |
| 14:45–14:55 | Paper | SQL-ML: A SQL-Centric Framework for Building Efficient Feature Store | Ahmad Ghazal, Hanumath Maduri, Pekka Kostamaa |
| 14:55–15:05 | Paper | A Low Latency Cache for Cloud RDBMs | Guohai Zhang, Xin Tang, Qingchen Chang, Huanchen Zhang, Kai Hwang, Yuesen Li, Runhuai Huang, Teng Wang, Wusheng Zhang, Ming Zhang, Qingchun Chen, Xiaodong Hou, Qian Wang |
| 15:05–15:15 | Paper | The Case for Intent-Based Query Rewriting | Gianna Lisa Nicolai, Patrick Hansert, Sebastian Michel |
| 15:15–15:25 | Paper | Lightweight Pipelines: Good Enough is Sometimes Better | Camilla Sancricca, Cinzia Cappiello |
| 15:25–15:35 | Break | Coffee Break | |
| 15:35–16:05 | Invited Talk | Databases as AI Runtimes | Rihan Hai |
| 16:05–16:35 | Invited Talk | AI-Driven Data Typing: Toward Semantic and Functional Understanding of Relational Data | Chang Ge |
| 16:35–16:45 | Paper | CleanAgent: Automating Data Standardization with LLM-based Agents | Danrui Qi, Zhengjie Miao, Jiannan Wang |
| 16:45–16:55 | Paper | SoAgent: A Real-world Data Empowered Agent Pool to Facilitate LLM-Driven Generative Social Simulation | Na Ta, Kaiyu Li, Yushu Zhou, Yuhan Liu |
| 16:55–17:05 | Paper | DeepSearch: LLM-powered Data Acquisition for Machine Learning | Kaiyu Li, Zhongxin Hu, Yuxin Gao, Yuyang Wu |
| 17:05–17:15 | Paper | Detecting and Cleaning Errors in Personal Contact Information with Large Language Models | Anna-Christina Glock, Christine Dominka-Kiss, Philipp Korom, Lisa Ehrlinger |
Abstract
The advent of artificial intelligence (AI), particularly through deep learning (DL) and large language models (LLMs), has marked a significant milestone in technological advancement, attributing to its unparalleled accuracy and generalization abilities. The rapid evolution of AI model structures to achieve superior performance underscores the dynamic progression and potential of AI technologies. However, the cornerstone of any AI's success lies not just in its algorithmic prowess but in the quality of data it is trained on. High-quality, accurate, consistent, and representative data sets are imperative for enhancing AI models' learning efficacy, thereby optimizing their generalization capabilities and reducing computational demands.
Beyond just leveraging quality data, AI technology itself plays a pivotal role in enhancing data quality through its powerful tools for data management. From cleaning, labeling, and validation to sophisticated feature engineering, AI ensures data accuracy, integrity, consistency, and reliability. This creates a symbiotic relationship between AI technology and high-quality data, highlighting their mutual dependence and the complementary nature of their interaction. It is this synergy that the 2nd International Workshop on Data-driven AI (DATAI) aims to explore, delving into the latest research breakthroughs and presenting innovative techniques and methodologies at the forefront of data-driven AI.
This workshop is dedicated to fostering a comprehensive understanding of the intricate relationship between AI technologies and the data they depend on, focusing on the development of high-quality data specifically tailored for AI technologies, with a particular emphasis on large-scale models. Through engaging researchers, developers, and practitioners in rigorous discussions, the workshop seeks to explore sustained advancements, design innovations, and practical applications of data construction techniques that propel the progress of AI technologies forward.
Topics of Interest
Relevant topics include, but are not limited to:
- Data discovery for AI.
- AI (LLMs)-driven data discovery.
- Data cleaning & integration for AI.
- Data quality for AI in time series data.
- AI for data system.
- AI (LLMs)-driven data cleaning & integration.
- LLM-based data extraction.
- AI (LLMs)-driven data transformation.
- Data selection for AI, including LLMs pre-training & SFT.
- Data management during the lifecycle of AI models.
- Labeling quality vs. AI performance.
- LLM-based data labeling.
- Data-efficient AI.
By fostering a collaborative environment, DATAI aims to inspire a diverse audience of
participants from the
realms of AI and data quality management, facilitating an exchange of ideas that propels the
field toward
groundbreaking developments.
Important Dates
More Details
- Submission Deadline for Research Papers: June 01, 2025
- Notification of Authors: June 20, 2025
- Camera-ready Version of Accepted Papers: July 01, 2025
Paper Submission Methods
Papers must be submitted via the EasyChair conference system, accessible at the following link:Easychair Cmt .
Historical Information
The 1st International Workshop on Data-driven AI (DATAI 2024)
Contact Information
For further inquiries, please contact the chairs through the provided email addresses in the official document[View PDF].
- Hongzhi Wang: wangzh@hit.edu.cn
- Nan Tang: nantang@hkust-gz.edu.cn