  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.
