activity
20242026
collaborators

12 papers

cs.DB2026

Cost-Effective Numerical QA over Semi-Structured Table: Structuring, Resolution, Planning

Feng Luo, Hui Luo, Zhifeng Bao +3

Semi-structured tables encode rich semantic information through diverse layout elements, such as hierarchical row and column headers. Answering numerical questions over such data i…

cs.IR2026

TaxoConf: Taxonomy-Guided Automatic Conference Program Organization

Daomin Ji, Zhifeng Bao, Junhao Gan

Conference program organization, the task of assembling accepted papers into a technical program, is labor-intensive. Papers must be grouped into topically coherent sessions under…

cs.DB2026

GRAFT: Graph-Matched Retrieval and Fusion of Tables in Data Lakes

Daomin Ji, Hui Luo, Zhifeng Bao +2

Autonomous data agents resolve analytical queries by retrieving and reasoning over evidence in tabular data lakes. Existing methods score tables independently against the query and…

cs.CL2026

Alignment-Guided Largest Table Overlap Size Estimation

Ge Lee, Shixun Huang, Zhifeng Bao +2

Fast estimation of the size of the largest overlap between tables enables blocking and query-by-table retrieval in large table repositories. The first and the state-of-the-art esti…

cs.DB2026

LEARNT: A Practical Estimator for Cardinality of LIKE Queries with Formal Accuracy Guarantees

Hai Lan, Zhifeng Bao, Divesh Srivastava +3

We study the problem of cardinality estimation for LIKE queries on string data, focusing on the most common patterns in real workloads: prefix, suffix, and substring queries. We pr…

cs.DB2026

Unified Data Discovery across Query Modalities and User Intents

Tingting Wang, Shixun Huang, Zhifeng Bao +4

Data discovery - retrieving relevant tables from a data lake in response to user queries - is a fundamental building block for downstream analytics. In practice, data discovery mus…

cs.DB2026

Shape-Agnostic Table Overlap Discovery: A Maximum Common Subhypergraph Approach

Ge Lee, Shixun Huang, Zhifeng Bao +3

Understanding how two tables overlap is useful for many data management tasks, but challenging because tables often differ in row and column orders and lack reliable metadata in pr…

cs.DB2026

Decomposition-Driven Multi-Table Retrieval and Reasoning for Numerical Question Answering

Feng Luo, Hai Lan, Hui Luo +4

In this paper, we study the problem of numerical multi-table question answering (MTQA) over large-scale table collections (e.g., online data repositories). This task is essential i…

cs.IR2025

A Survey on Open Dataset Search in the LLM Era: Retrospectives and Perspectives

Pengyue Li, Sheng Wang, Hua Dai +3

High-quality datasets are typically required for accomplishing data-driven tasks, such as training medical diagnosis models, predicting real-time traffic conditions, or conducting…

cs.DB2024

Table Integration in Data Lakes Unleashed: Pairwise Integrability Judgment, Integrable Set Discovery, and Multi-Tuple Conflict Resolution

Daomin Ji, Hui Luo, Zhifeng Bao +1

Table integration aims to create a comprehensive table by consolidating tuples containing relevant information. In this work, we investigate the challenge of integrating multiple t…

cs.DB2024

Dataset Discovery via Line Charts

Daomin Ji, Hui Luo, Zhifeng Bao +1

Line charts are a valuable tool for data analysis and exploration, distilling essential insights from a dataset. However, access to the underlying dataset behind a line chart is ra…

cs.DB2024

Distinctiveness Maximization in Datasets Assemblage

Tingting Wang, Shixun Huang, Zhifeng Bao +3

In this paper, given a user's query set and budget, we aim to use the limited budget to help users assemble a set of datasets that can enrich a base dataset by introducing the maxi…