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ID topic:	220
Partner Email:	stavros@cs.uoi.gr
Project Title:	Similarity Measures for Multidimensional Data
Abstract:	How similar are two data-cubes? In other words, the question under consideration is: given two sets of points in a multidimensional hierarchical space, what is the distance value between them? This thesis explores various distance functions that can be used over multidimensional hierarchical spaces in order to decide which is the best distance function for evaluating the similarity of two sets of points. We organize the discussed functions with respect to the properties of the dimension hierarchies, levels and values. In order to discover which distance functions are more suitable and meaningful to the users, two user studies are necessary. The first user study analysis concerns the most preferred distance function between two values of a dimension. The goal of the second user study involves discovering which distance function between two data cubes, is mostly preferred by users.
Advisor:	Stavros Nikolopoulos
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Degree:	Master
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