Investigating variety in similarity queries

Abstract

The data being collected and generated nowadays increases not only in volume, but also in complexity, leading to the need of new query operators. Similarity queries are one of the most pursued resources to retrieve complex data. The most studied operators to perform similarity are the Range query and the $k$-Nearest Neighbor query. Until recently, those queries were not available in the Database Management Systems. Now they are stating to become available, but its first uses to develop real systems make it clear that the basic similarity query operators are not enough to meet the requirements of the target applications. Therefore, new variations and extensions to the basic operators are being studied, pursuing the requirements of specific application domains. This project aims at studying and developing new variations for similarity query operators targeting two main application domains: images of medical exams to help developing computer-aided diagnosis tools, and remote sensing images and time series to help agriculture and climate changes studies. The new operator variations will be developed aiming to meet the requirement already evidenced developing real applications for those domains, namely: the user's desire to obtain answers that include more variety and also that more closely follows his/her expectations, even when those expectations are not clearly stated in the query command. (AU)