Data warehouses structure data in multidimensional cubes, where dimensions specify different ways in which measures in facts may be viewed, aggregated, and sorted. It is essential for data analysts to combine data from heterogeneous multidimensional cubes to enhance their analysis capabilities. For this, users are restricted in using only shared dimensions for navigating related multidimensiona...

In this paper we propose numerical measures for evaluating the aesthetic interest of simple patterns. The patterns consist of elements (symbols, pixels, etc) in regular square arrays. The measures depend on two characteristics of the patterns: the number of different types of element, and the number of symmetries in their arrangement. We define two complementary composite measures L and C for t...

As the number and complexity of visualization techniques have grown, it has become progressively more difficult to make a decision as to which technique to employ for any given situation or application. A particular case is that of multidimensional data visualization utilizing projections, which have gained much attention lately and are being utilized in a growing number of applications. With t...

We present two extensions of the algorithm by Broomhead et al [2] which is based on the idea that singular values that scale linearly with the radius of the data ball can be exploited to develop algorithms for computing topological dimension and for detecting whether data models based on manifolds are appropriate. We present a geometric scaling property and dimensionality criterion that permit ...

Visualizing and structuring pairwise dissimilarity data are difficult combinatorial optimization problems known as multidimensional scaling or pairwise data clustering. Algorithms for embedding dissimilarity data set in a Euclidian space, for clustering these data and for actively selecting data to support the clustering process are discussed in the maximum entropy framework. Active data select...