We extend the concept of worst-case optimal equijoins in graph databases to the case where some nodes are required to be within the k-nearest neighbors (kNN) of others under some similarity function. We model the problem ...

Similarity join is a key operation in metric databases. It retrieves all pairs of elements that are similar. Solving such a problem usually requires comparing every pair of objects of the datasets, even when indexing and ...

Near-duplicate image detection plays an important role in several real applications. Such task is usually achieved by applying a clustering algorithm followed by refinement steps, which is a computationally expensive ...

Similarity joins are troublesome database operators that often produce results much larger than the user really needs or expects. In order to return the similar elements, similarity joins also require sorting during the ...

With the increasing ability of current applications to produce and consume more complex data, such as images and geographic information, the similarity join has attracted considerable attention. However, this operator does ...

Crowdsourcing information is being increasingly employed to improve and support decision making in emergency situations. However, the gathered records quickly become too similar among themselves and handling several similar ...

Many modern applications deal with complex data, where retrieval by similarity plays an important role. Complex data main comparison mechanisms are based on similarity predicates. They are usually immersed in metric spaces ...

Multiple descriptors are employed to represent images in Content-Based Image Retrieval (CBIR) systems. Each descriptor consists of a feature extractor associated with a distance function. An extractor is generally suitable ...