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Scientific data sets continue to increase in both size and complexity. In the past, dedicated graphics systems at supercomputing centers were required to visualize large data sets, but as the price of commodity graphics hardware has dropped and its capability has increased, it is now possible, in principle, to view large complex data sets on a single workstation. To do this in practice, an investigator will need software that is written to take advantage of the relevant graphics hardware. The Viewpoints visualization package described herein is an example of such software. Viewpoints is an interactive tool for exploratory visual analysis of large high-dimensional (multivariate) data. It leverages the capabilities of modern graphics boards (GPUs) to run on a single workstation or laptop. Viewpoints is minimalist: it attempts to do a small set of useful things very well (or at least very quickly) in comparison with similar packages today. Its basic feature set includes linked scatter plots with brushing, dynamic histograms, normalization, and outlier detection/removal. Viewpoints was originally designed for astrophysicists, but it has since been used in a variety of fields that range from astronomy, quantum chemistry, fluid dynamics, machine learning, bioinformatics, and finance to information technology server log mining. In this article, we describe the Viewpoints package and show examples of its usage.

A model reduction technique based on parameter matching for single input single output (SISO) continuous systems is proposed. The reduced order models match various combinations (chosen by the designer) of four types of system invariant parameter of the full order system associated with low frequency response, high frequency response, low frequency power spectral density and high frequency power spectral density. In this way, selected parameters of both transient and steady state properties can be preserved in the model reduction process. Which parameters are chosen to be matched is contingent on those input/output properties of the original system that the designer considers most important to be retained in the reduced model. The number of parameters to be matched determines the order of the reduced model. The effectiveness of the matched parameters in retaining the input/output properties of the original system in the reduced models is fully investigated. The algorithm presented finds not one but the entire set (infinite) of reduced models that match a particular number of these parameters. Because the mix of parameters desired (i.e. the algorithm chosen) is at the discretion of the analyst, the new algorithm presented provides a handy assortment of techniques instead of a single 'finished product'.