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Paolo Ciaccia
Researcher at University of Bologna
Publications - 124
Citations - 4389
Paolo Ciaccia is an academic researcher from University of Bologna. The author has contributed to research in topics: Nearest neighbor search & Skyline. The author has an hindex of 26, co-authored 122 publications receiving 4212 citations.
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Proceedings Article
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
TL;DR: The results demonstrate that the Mtree indeed extends the domain of applicability beyond the traditional vector spaces, performs reasonably well in high-dimensional data spaces, and scales well in case of growing files.
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WARP: accurate retrieval of shapes using phase of Fourier descriptors and time warping distance
TL;DR: This work proposes a novel Fourier-based approach, called WARP, for matching and retrieving similar shapes, which exploits the phase of Fourier coefficients and the use of the dynamic time warping distance to compare shape descriptors.
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Efficient sort-based skyline evaluation
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.
Proceedings ArticleDOI
PAC nearest neighbor queries: Approximate and controlled search in high-dimensional and metric spaces
Paolo Ciaccia,Marco Patella +1 more
TL;DR: This paper describes sequential and index-based PAC-NN algorithms that exploit the distance distribution of the query object in order to determine a stopping condition that respects the error bound, and provides experimental evidence that indexing can further speed-up the retrieval process by up to 1-2 orders of magnitude without giving up the accuracy of the result.
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SaLSa: computing the skyline without scanning the whole sky
TL;DR: SaLSa (Sort and Limit Skyline algorithm), which exploits the sorting machinery of a relational engine to order tuples so that only a subset of them needs to be examined for computing the skyline result.