Spatial Similarity Queries with Logical Operators

Abstract

Traditional spatial querying assumes that a user specifies exactly the constraints of valid results, and that the result set contains only those items that fulfill exactly the query constraints. The nature of spatial data, however, makes it difficult for a user to always guess correctly the values stored, while exhaustive enumerations of acceptable alternatives to the ideal target would become a tedious process. Likewise, values that deviate somewhat from the query constraints should be part of a ranked result set as well. This paper develops methods for the retrieval of similar spatial information, in particular when several similarity constraints with logical combinations must be compared and integrated. The first set of methods is concerned with spatial similarity reasoning over null values by denoting the semantics of different types of null values with explicit identifiers that imply different degrees of uncertainty. The second set of methods contributes to a consistent and comprehensive methodology for spatial similarity retrieval in response to complex queries with combinations of logical operators. We provide an exhaustive list of spatial query scenarios with conjunctions, disjunctions, and negation and present justified solutions for each case. Since these computational methods are founded on fundamental psychological knowledge about similarity reasoning, they are particularly well suited for generating similarity results that match with users’ intuitions.