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Though data cleaning systems have earned great success and wide spread inboth 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 onfunctional dependency rules where there is sufficient co-occurrence of valuepairs to learn that a certain value of an attribute leads to a correspondingvalue of another attribute. However, for spatial attributes that representlocations on the form of <latitude, longitude>, there is very little chancethat two records would have the same exact coordinates, and hence co-occurrencewould unlikely to exist. This paper presents Sparcle~(SPatially-AwaReCLEaning); a novel framework that injects spatial awareness into the coreengine of rule-based data cleaning systems as a means of boosting theiraccuracy. Sparcle injects two main spatial concepts into the core engine ofdata cleaning systems: (1) Spatial Neighborhood, where co-occurrence is relaxedto be within a certain spatial proximity rather than same exact value, and (2)Distance Weighting, where records are given different weights of whether theysatisfy a dependency rule, based on their relative distance. Experimentalresults using a real deployment of Sparcle inside a state-of-the-art datacleaning system, and real and synthetic datasets, show that Sparclesignificantly boosts the accuracy of data cleaning systems when dealing withspatial data.

Sparcle: Boosting the Accuracy of Data Cleaning Systems through Spatial Awareness