Papers › Sparcle: Boosting the Accuracy of Data Cleaning Systems through Spatial Awareness
Sparcle: Boosting the Accuracy of Data Cleaning Systems through Spatial Awareness
Yuchuan Huang, Mohamed F. Mokbel
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Though data cleaning systems have earned great success and wide spread in both academia and industry, they fall short when trying to clean spatial data. The main reason is that state-of-the-art data cleaning systems mainly rely on functional dependency rules where there is sufficient co-occurrence of value pairs to learn that a certain value of an attribute leads to a corresponding value of another attribute. However, for spatial attributes that represent locations on the form of <latitude, longitude>, there is very little chance that two records would have the same exact coordinates, and hence co-occurrence would unlikely to exist. This paper presents Sparcle~(SPatially-AwaRe CLEaning); a novel framework that injects spatial awareness into the core engine of rule-based data cleaning systems as a means of boosting their accuracy. Sparcle injects two main spatial concepts into the core engine of data cleaning systems: (1) Spatial Neighborhood, where co-occurrence is relaxed to be within a certain spatial proximity rather than same exact value, and (2) Distance Weighting, where records are given different weights of whether they satisfy a dependency rule, based on their relative distance. Experimental results using a real deployment of Sparcle inside a state-of-the-art data cleaning system, and real and synthetic datasets, show that Sparcle significantly boosts the accuracy of data cleaning systems when dealing with spatial data.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections