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dc.contributor.authorSkala, Matthew Adam 14:43:31 (GMT) 14:43:31 (GMT)
dc.description.abstractMetric spaces, which generalise the properties of commonly-encountered physical and abstract spaces into a mathematical framework, frequently occur in computer science applications. Three major kinds of questions about metric spaces are considered here: the intrinsic dimensionality of a distribution, the maximum number of distance permutations, and the difficulty of reverse similarity search. Intrinsic dimensionality measures the tendency for points to be equidistant, which is diagnostic of high-dimensional spaces. Distance permutations describe the order in which a set of fixed sites appears while moving away from a chosen point; the number of distinct permutations determines the amount of storage space required by some kinds of indexing data structure. Reverse similarity search problems are constraint satisfaction problems derived from distance-based index structures. Their difficulty reveals details of the structure of the space. Theoretical and experimental results are given for these three questions in a wide range of metric spaces, with commentary on the consequences for computer science applications and additional related results where appropriate.en
dc.publisherUniversity of Waterlooen
dc.subjectmetric spaceen
dc.subjectrobust hashen
dc.subjectintrinsic dimensionalityen
dc.titleAspects of Metric Spaces in Computationen
dc.typeDoctoral Thesisen
dc.subject.programComputer Scienceen of Computer Scienceen
uws-etd.degreeDoctor of Philosophyen

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