DASFAA2024.github.io
International Conference on Database Systems for Advanced Applications
Workshop:
The 10th International Workshop on Big Data Management and Service (02/07/2024, Gifu, Japan)
Program
8:55-9:00 Opening
9:00-9:30 Invited Talk: GoodCore: Data-effective and Data-efficient Machine Learning through Coreset Selection over Incomplete Data
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Speaker: Chengliang Chai (Associate professor, Beijing Institute of Technology, China)
- Biography: Chengliang Chai is currently an associate professor in Beijing Institute of Technology, Beijing, China. He received the PH.D. de gree in computer science and technology from Tsinghua University. His research interests include crowdsourcing data manage ment, data mining and database. He has publised more than 50 papers from top-tier conference like SIGMOD, VLDB, ICDE, etc. He won the CCF doctoral dissertation award and ACM China doctoral dissertation award.
- Abstract: Given a dataset with incomplete data (e.g., missing values), training a machine learning model over the incomplete data requires two steps. First, it requires a data-effective step that cleans the data in order to improve the data quality (and the model quality on the cleaned data). Second, it requires a data-efficient step that selects a core subset of the data (called coreset) such that the trained models on the entire data and the coreset have similar model quality, in order to improve the training efficiency. The first-data-effective-then-data-efficient methods are too costly, because they are expensive to clean the whole data; while the first-data-efficient-then-data-effective methods have low model quality, because they cannot select high-quality coreset for incomplete data. We investigate the problem of coreset selection over incomplete data for data-effective and data-efficient machine learning. The essential challenge is how to model the incomplete data for selecting high-quality coreset. To this end, we propose the GoodCore framework towards selecting a good coreset over incomplete data with low cost. To model the unknown complete data, we utilize the combinations of possible repairs as possible worlds of the incomplete data. Based on possible worlds, GoodCore selects an expected optimal coreset through gradient approximation without training ML models. We formally define the expected optimal coreset selection problem, prove its NP-hardness, and propose a greedy algorithm with an approximation ratio. To make GoodCore more efficient, we further propose optimization methods that incorporate human-in-the-loop imputation or automatic imputation method into our framework. Experimental results show the effectiveness and efficiency of our framework with low cost.
9:30-9:45 FAITH: A Fast, Accurate, and Lightweight Database-agnostic Learned Cost Model
- Waner Li (University of Electronic Science and Technology of China); Xu Chen (University of Electronic Science and Technology of China); Zibo Liang (University of Electronic Science and Technology of China); Runfan Ye (University of Electronic Science and Technology of China); Ruyi Lai (University of Electronic Science and Technology of China); Siying Yue (Huaxin Consulting Co., Ltd); Qinyuan Su (China Telecom Co., Ltd); Kai Zheng (University of Electronic Science and Technology of China); Han Su (University of Electronic Science and Technology of China); Shaozhi Wu (University of Electronic Science and Technology of China)
9:45-10:00 Financial-ICS: Identifying Peer Firms via LongBERT from 10K Reports
- Jintao Huang (University of Science and Technology of China)
10:00-10:15 Exploring the application of nonlinear paral differential equations in computer numerical simulation operating systems
- Yongcun Shao (Suzhou City University)
10:15-10:30 Evolution-Driven Deep Meta-Learning Algorithm for Multi-Objective Combinatorial Optimization
- jiajun Chen (South China University of Technology); Shaoxin Tan (South China University of Technology)
10:30-11:00 Coffee Break
11:00-11:15 Fast Approximate Temporal Buterfly Counting on Bipartite Graphs via Edge Sampling
- Jiaxi Pu (East China Normal University); Yanhao Wang (East China Normal University); Yuchen Li (Singapore Management University); Xuan Zhou (East China Normal University)
11:15-11:30 Analysis of Kinematics and Dynamics for Mobile Robots under Nonholonomic Constraints
- Yongcun Shao (Suzhou City University)
Organizing Team
- Dr. Yan Zhao, Aalborg University, Denmark
- Prof. Xiaoling Wang, East China Normal University, China
- Prof. Kai Zheng, University of Electronic Science and Technology of China