The increasing volume of multimedia data stored in relational database management systems (RDBMS) demands efficient ways to process similarity queries. Therefore, the query processor should provide mechanisms to express similarity queries, to interpret and translate them into equivalent expression in relational algebra, to evaluate alternative query plans and finally to execute the queries using the best plan found. In this paper, we present an effective framework to interpret, translate, select the best plan and efficiently execute similarity queries over data indexed by metric access methods. Experimental evaluation of the framework shows a reduction of up to 20% in the total time required to answer similarity queries.
Large amounts of images from medical exams are being stored in databases, so developing retrieval techniques is an important research problem. Retrieval based on the image visual content is usually better than using textual descriptions, as they seldom gives every nuances that the user may be interested in. Content-based image retrieval employs the similarity among images for retrieval. However, similarity is evaluated using numeric methods, and they often orders the images by similarity in a way rather distinct from the user's intention. In this paper, we propose a technique to allow expressing the user's preference over attributes associated to the images, so similarity queries can be refined by preference rules. Experiments performed over a dataset with CT lung images shows that correctly expressing the user's preferences, the similarity query precision can increase from an average of 60% up to close to 100%, when enough interesting images exists in the database.
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