7 citations · 15 across the 7 of their papers we have counts for
9 papers · 1 filter
Auto-Relate: A Unified Approach to Discovering Reliable Functional Relationships Leveraging Statistical Tests
Ziyan Han, Yeye He, Shuyuan Kang +8
Tables in spreadsheets, computational notebooks, and databases often contain rich inter-column relationships. Yet these relationships are typically implicit and are often lost when…
Nexus: Inferring Join Graphs from Metadata Alone via Iterative Low-Rank Matrix Completion
Tianji Cong, Yuanyuan Tian, Andreas Mueller +5
Automatically inferring join relationships is a critical task for effective data discovery, integration, querying and reuse. However, accurately and efficiently identifying these r…
Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples
Peng Li, Yeye He, Cong Yan +2
Relational tables, where each row corresponds to an entity and each column corresponds to an attribute, have been the standard for tables in relational databases. However, such a s…
Ground Truth Inference for Weakly Supervised Entity Matching
Renzhi Wu, Alexander Bendeck, Xu Chu +1
Entity matching (EM) refers to the problem of identifying pairs of data records in one or more relational tables that refer to the same entity in the real world. Supervised machine…
Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and Search
Junwen Yang, Yeye He, Surajit Chaudhuri
Recent work has made significant progress in helping users to automate single data preparation steps, such as string-transformations and table-manipulation operators (e.g., Join, G…
Demonstration of Panda: A Weakly Supervised Entity Matching System
Renzhi Wu, Prem Sakala, Peng Li +2
Entity matching (EM) refers to the problem of identifying tuple pairs in one or more relations that refer to the same real world entities. Supervised machine learning (ML) approach…