From the 1 of 10 linked papers with an AI index.
10 papers
GRAFT: Graph-Matched Retrieval and Fusion of Tables in Data Lakes
Daomin Ji, Hui Luo, Zhifeng Bao +2
The paper introduces GRAFT, a system that retrieves and combines tables from large data lakes by matching query intent graphs to a heterogeneous data lake graph and using reinforce…
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…
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…