Nikos Mamoulis

TL;DR: This paper derives a level-wise mining algorithm that makes use of the SG-tree (signature tree) and some effective pruning techniques to efficiently discover all MIS, which is an infrequent structure while all proper subtrees of it are frequent.

TL;DR: AIR-tree as discussed by the authors is the first method for the adaptive indexing of non-point spatial objects, which incrementally and progressively constructs an in-memory spatial index over a static array, in response to incoming queries, using a suite of heuristics for creating and splitting nodes.

TL;DR: This paper advances the state-of-the-art by combining existing approaches to a hybrid nearest neighbor-based method while also proposing an alternative, more efficient spatial range-based approach and investigating the continuous counterpart of distance-to-points trajectory search.

TL;DR: This paper proposes an effective spatial proximity measure between a query issuer and a query with a location distribution obtained from its clicked URLs in the query history, and extends popular query recommendation and auto-completion approaches to the authors' location-aware setting, which suggest query reformulations that are semantically relevant to the original query and give results that are spatially close to the query issuer.

TL;DR: This paper determines relevancy by comparing the geospatial relatedness of triplesets containing instances belonging to spatial classes based on the hypothesis that pairs of classes whose instances present similar spatial distribution are likely to contain semantically related instances.