Sparcle: Boosting the Accuracy of Data Cleaning Systems through Spatial Awareness
Summary: Sparcle augments rule-based data cleaning for spatial attributes by relaxing co-occurrence to spatial neighborhoods and applying distance-weighted rule satisfaction. Integrated into a state-of-the-art cleaner, it improves cleaning accuracy on real and synthetic spatial datasets. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,081 | From Suspicious Errors to Valid Data: On Repairing Spatio-Temporal Data via Spatial and Temporal Dependencies | 2026 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 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 |
|---|---|---|---|---|
| 2,302 | Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions | 2021 | VLDB | 9.0668832e-05 |
| 6,012 | Clue-based Spatio-textual Query | 2017 | VLDB | 5.2415551e-05 |
| 10,930 | Similarity Joins of Sparse Features | 2024 | SIGMOD | 4.1945683e-05 |
| 7,645 | Selectivity Estimation on Streaming Spatio-Textual Data Using Local Correlations | 2015 | VLDB | 4.6896215e-05 |
| 11,138 | Neighborhood-Preserving Graph Sparsification | 2024 | VLDB | 4.1945683e-05 |
| 4,650 | LocationSpark: A Distributed In-Memory Data Management System for Big Spatial Data | 2016 | VLDB | 6.0234336e-05 |
| 2,797 | Query-Oriented Data Cleaning with Oracles | 2015 | SIGMOD | 8.1108589e-05 |
| 4,904 | Temporal Rules Discovery for Web Data Cleaning | 2016 | VLDB | 5.8399195e-05 |
| 10,081 | From Suspicious Errors to Valid Data: On Repairing Spatio-Temporal Data via Spatial and Temporal Dependencies | 2026 | SIGMOD | 4.1945683e-05 |
| 9,898 | Top-k Relevant Semantic Place Retrieval on Spatial RDF Data | 2016 | SIGMOD | 4.2600049e-05 |