Showing papers by "Paolo Ciaccia published in 2008"
TL;DR: Salinas as discussed by the authors is a novel skyline algorithm that exploits the idea of presorting the input data so as to effectively limit the number of tuples to be read and compared, which makes salsa also attractive when skyline queries are executed on top of systems that do not understand skyline semantics.
Abstract: Skyline queries compute the set of Pareto-optimal tuples in a relation, that is, those tuples that are not dominated by any other tuple in the same relation. Although several algorithms have been proposed for efficiently evaluating skyline queries, they either necessitate the relation to have been indexed or have to perform the dominance tests on all the tuples in order to determine the result. In this article we introduce salsa, a novel skyline algorithm that exploits the idea of presorting the input data so as to effectively limit the number of tuples to be read and compared. This makes salsa also attractive when skyline queries are executed on top of systems that do not understand skyline semantics, or when the skyline logic runs on clients with limited power and/or bandwidth. We prove that, if one considers symmetric sorting functions, the number of tuples to be read is minimized by sorting data according to a “minimum coordinate,” minC, criterion, and that performance can be further improved if data distribution is known and an asymmetric sorting function is used. Experimental results obtained on synthetic and real datasets show that salsa consistently outperforms state-of-the-art sequential skyline algorithms and that its performance can be accurately predicted.
206 citations
11 Apr 2008
TL;DR: This article reviews the major paradigms for approximate similarity queries and proposes a classification schema that easily allows existing approaches to be compared along several independent coordinates and shows that no provable optimal scheduling strategy exists for approximate queries.
Abstract: In this article, we review the major paradigms for approximate similarity queries and propose a classification schema that easily allows existing approaches to be compared along several independent coordinates. Then, we discuss the impact that scheduling of index nodes can have on performance and show that, unlike exact similarity queries, no provable optimal scheduling strategy exists for approximate queries. On the positive side, we show that optimal- on-the-average schedules are well-defined. We complete by critically reviewing methods for evaluating the quality of approximate results.
21 citations
28 May 2008
TL;DR: This demo presents Scenique, a multimodal image retrieval system that provides the user with two basic facilities: an image annotator that is able to predict keywords for new images, and an integrated query facility that allows the user to search for images using both visual features and tags, possibly organized in semantic dimensions.
Abstract: Searching for images by using low-level visual features, such as color and texture, is known to be a powerful, yet imprecise, retrieval paradigm. The same is true if search relies only on keywords (or tags), either derived from the image context or user-provided annotations. In this demo we present Scenique, a multimodal image retrieval system that provides the user with two basic facilities: 1) an image annotator, that is able to predict keywords for new (i.e., unlabelled) images, and 2) an integrated query facility that allows the user to search for images using both visual features and tags, possibly organized in semantic dimensions. We demonstrate the accuracy of image annotation and the improved precision that Scenique obtains with respect to querying with either only features or keywords.
13 citations