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Auto-Join: Joining Tables by Leveraging Transformations
Summary: Auto-Join searches operator space to synthesize a transformation that renders join-columns across representations equi-joinable. An optimal sampling strategy scales to large data and yields high-probability joins, validated on web and enterprise tables.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 11391
- Venue
- VLDB
- Year
- 2017
- Pagerank
- 6.8006812e-05
- Overall Rank
- 3,738 | 74.03%
- DOI
-
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Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 25 of 25 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 1,914 |
Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks |
2020 |
SIGMOD |
0.00010111859 |
| 2,161 |
Uni-Detect: A Unified Approach to Automated Error Detection in Tables |
2019 |
SIGMOD |
9.4029915e-05 |
| 2,585 |
Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks |
2024 |
SIGMOD |
8.4909917e-05 |
| 2,737 |
Open Data Integration |
2018 |
VLDB |
8.2053894e-05 |
| 3,254 |
Auto-Suggest: Learning-to-Recommend Data Preparation Steps Using Data Science Notebooks |
2020 |
SIGMOD |
7.3172324e-05 |
| 3,326 |
DeepJoin: Joinable Table Discovery with Pre-trained Language Models |
2023 |
VLDB |
7.2148323e-05 |
| 3,483 |
Transform-Data-by-Example (TDE): An Extensible Search Engine for Data Transformations |
2018 |
VLDB |
7.0493668e-05 |
| 4,860 |
Integrating Data Lake Tables |
2023 |
VLDB |
5.867964e-05 |
| 5,096 |
Auto-Transform: Learning-to-Transform by Patterns |
2020 |
VLDB |
5.6960764e-05 |
| 5,279 |
Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples |
2023 |
VLDB |
5.5851818e-05 |
| 5,283 |
Explaining Dataset Changes for Semantic Data Versioning with Explain-Da-V |
2023 |
VLDB |
5.5843052e-05 |
| 5,388 |
Auto-FuzzyJoin: Auto-Program Fuzzy Similarity Joins Without Labeled Examples |
2021 |
SIGMOD |
5.5342724e-05 |
| 5,389 |
Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and Search |
2021 |
VLDB |
5.5339832e-05 |
| 5,706 |
Putting Things into Context: Rich Explanations for Query Answers using Join Graphs |
2021 |
SIGMOD |
5.3633001e-05 |
| 6,268 |
MATE: Multi-Attribute Table Extraction |
2022 |
VLDB |
5.1288179e-05 |
| 6,798 |
DTT: An Example-Driven Tabular Transformer for Joinability by Leveraging Large Language Models |
2024 |
SIGMOD |
4.9186164e-05 |
| 7,642 |
Cross Modal Data Discovery over Structured and Unstructured Data Lakes |
2023 |
VLDB |
4.6856127e-05 |
| 7,859 |
ConnectionLens: Finding Connections Across Heterogeneous Data Sources |
2018 |
VLDB |
4.6298049e-05 |
| 9,143 |
Design and Analysis of a Processing-in-DIMM Join Algorithm: A Case Study with UPMEM DIMMs |
2023 |
SIGMOD |
4.381112e-05 |
| 9,381 |
Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations |
2024 |
SIGMOD |
4.343902e-05 |
| 9,405 |
TabulaX: Leveraging Large Language Models for Multi-Class Table Transformations |
2025 |
VLDB |
4.3399748e-05 |
| 9,491 |
Auto-BI: Automatically Build BI-Models Leveraging Local Join Prediction and Global Schema Graph |
2023 |
VLDB |
4.3300131e-05 |
| 10,603 |
Optimized Batch Prompting for Cost-effective LLMs |
2025 |
VLDB |
4.1905499e-05 |
| 10,606 |
Auto-Prep: Holistic Prediction of Data Preparation Steps for Self-Service Business Intelligence |
2025 |
VLDB |
4.1905499e-05 |
| 11,345 |
SPINE: Scaling up Programming-by-Negative-Example for String Filtering and Transformation |
2022 |
SIGMOD |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 2,729 |
String Similarity Joins: An Experimental Evaluation |
2014 |
VLDB |
8.2175463e-05 |
| 1,575 |
Reverse Engineering Complex Join Queries |
2013 |
SIGMOD |
0.00011288804 |
| 9,491 |
Auto-BI: Automatically Build BI-Models Leveraging Local Join Prediction and Global Schema Graph |
2023 |
VLDB |
4.3300131e-05 |
| 3,326 |
DeepJoin: Joinable Table Discovery with Pre-trained Language Models |
2023 |
VLDB |
7.2148323e-05 |
| 5,096 |
Auto-Transform: Learning-to-Transform by Patterns |
2020 |
VLDB |
5.6960764e-05 |
| 10,606 |
Auto-Prep: Holistic Prediction of Data Preparation Steps for Self-Service Business Intelligence |
2025 |
VLDB |
4.1905499e-05 |
| 6,798 |
DTT: An Example-Driven Tabular Transformer for Joinability by Leveraging Large Language Models |
2024 |
SIGMOD |
4.9186164e-05 |
| 5,388 |
Auto-FuzzyJoin: Auto-Program Fuzzy Similarity Joins Without Labeled Examples |
2021 |
SIGMOD |
5.5342724e-05 |
| 4,849 |
SEMA-JOIN: Joining Semantically-Related Tables Using Big Table Corpora |
2015 |
VLDB |
5.872093e-05 |
| 5,279 |
Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples |
2023 |
VLDB |
5.5851818e-05 |