ReStore - Neural Data Completion for Relational Databases
Summary: ReStore uses neural, schema-structured completion for relational tables with missing tuples to synthesize plausible substitutes. It reduces aggregate-query error by up to 390% vs incomplete data alone, enabling automated OLAP without manual imputation. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,523 | Controllable Tabular Data Synthesis Using Diffusion Models | 2024 | SIGMOD | 4.4937074e-05 |
| 9,856 | In-Database Data Imputation | 2024 | SIGMOD | 4.269353e-05 |
| 11,054 | Enriching Relations with Additional Attributes for ER | 2024 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 14,258 | Completeness Information and Its Application to Query Processing | 1986 | VLDB | - |
| 9,479 | Data Imputation with Limited Data Redundancy Using Data Lakes | 2025 | VLDB | 4.3341665e-05 |
| 13,229 | Using Deep Learning Models to Replace Large Materialized Views in Relational Database | 2021 | CIDR | - |
| 11,161 | Querying Incomplete Numerical Data: Between Certain and Possible Answers | 2023 | PODS | 4.1945683e-05 |
| 1,009 | SnipSuggest: Context-Aware Autocompletion for SQL | 2011 | VLDB | 0.00014653644 |
| 1,549 | Spreadsheets in RDBMS for OLAP | 2003 | SIGMOD | 0.00011428835 |
| 4,661 | PreQR: Pre-training Representation for SQL Understanding | 2022 | SIGMOD | 6.0137947e-05 |
| 10,565 | Holistic query Approximation via RL Modeling | 2025 | VLDB | 4.1945683e-05 |
| 5,459 | Capturing Missing Tuples and Missing Values | 2010 | PODS | 5.4957751e-05 |
| 6,273 | Identifying the Extent of Completeness of Query Answers over Partially Complete Databases | 2015 | SIGMOD | 5.1323078e-05 |