8 papers · 1 filter
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…
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…
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…
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…
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…
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…