The ever-increasing popularity of mobile applications coupled with the prevalence of spatial data has created the need for efficient processing of spatial queries in mobile enviro...
Anirban Mondal, Anthony K. H. Tung, Masaru Kitsure...
To reason about geographical objects, it is not only necessary to have more or less complete information about where these objects are located in space, but also how they can chang...
Clustering is a fundamental task in Spatial Data Mining where data consists of observations for a site (e.g. areal units) descriptive of one or more (spatial) primary units, possib...
Donato Malerba, Annalisa Appice, Antonio Varlaro, ...
Many geographical applications deal with spatial objects that cannot be adequately described by determinate, crisp concepts because of their intrinsically indeterminate and vague ...
Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. Co-location pattern discovery presents challeng...
Human vision system actively seeks interesting regions in images to reduce the search effort in tasks, such as object detection and recognition. Similarly, prominent actions in v...
Retrieving data based not only on key words is a challenge. We worked on semi-structured data (cultural heritage corpora). Our project aimed at getting the most relevant text-unit...
Julien Lesbegueries, Christian Sallaberry, Mauro G...
Decision support systems (DSS) may be enhanced qualitatively if they are able to also deal with spatial dimensions and measures. Regardless the evident importance of using data wa...
Marcus Costa Sampaio, Andre Gomes de Sousa, Cl&aac...
The goal of spatial co-location pattern mining is to find subsets of spatial features frequently located together in spatial proximity. Example co-location patterns include servi...
This paper presents an extension to category classification with bag-of-features, which represents an image as an orderless distribution of features. We propose a method to explo...