We propose a new graph layout method based on a modification of the t‐distributed Stochastic Neighbor Embedding (t‐SNE) dimensionality reduction technique. Although t‐SNE is one of the best techniques for visualizing high‐dimensional data as 2D scatterplots, t‐SNE has not been used in the context of classical graph layout. We propose a new graph layout method, tsNET, based on representing a graph with a distance matrix, which together with a modified t‐SNE cost function results in desirable layouts. We evaluate our method by a formal comparison with state‐of‐the‐art methods, both visually and via established quality metrics on a comprehensive benchmark, containing real‐world and synthetic graphs. As evidenced by the quality metrics and visual inspection, tsNET produces excellent layouts.

Loose programming enables analysts to program with concepts instead of procedural code. Data transformations are left underspecified, leaving out procedural details and exploiting knowledge about the applicability of functions to data types. To synthesize workflows of high quality for a geo‐analytical task, the semantic type system needs to reflect knowledge of geographic information systems (GIS) at a level that is deep enough to capture geo‐analytical concepts and intentions, yet shallow enough to generalize over GIS implementations. Recently, core concepts of spatial information and related geo‐analytical concepts were proposed as a way to add the required abstraction level to current geodata models. The core concept data types (CCD) ontology is a semantic type system that can be used to constrain GIS functions for workflow synthesis. However, to date, it is unknown what gain in precision and workflow quality can be expected. In this article we synthesize workflows by annotating GIS tools with these types, specifying a range of common analytical tasks taken from an urban livability scenario. We measure the quality of automatically synthesized workflows against a benchmark generated from common data types. Results show that CCD concepts significantly improve the precision of workflow synthesis.

Abstract:Understanding large multidimensional datasets is one of the most challenging problems in visual data exploration. One key challenge that increases the size of the exploration space is the number of views that one can generate from a single dataset, based on the use of multiple parameter values and exploration paths. Often, no such single view contains all needed insights. The question thus arises of how we can efficiently combine insights from multiple views of a dataset. We propose a set of techniques that considerably reduce the exploration effort for such situations, based on the explicit depiction of the view space, using a small multiple metaphor. We leverage this view space by offering interactive techniques that enable users to explicitly create, visualize, and follow their exploration path. This way, partial insights obtained from each view can be efficiently and effectively combined. We demonstrate our approach by applications using real-world datasets from air traffic control, software maintenance, and machine learning.

We examine the problem of sampling noise encountered in time-domain simulations of linear and two-dimensional spectroscopies. A new adaptive apodization scheme based on physical arguments is devised for suppressing the noise in order to allow reducing the number of used disorder realisations, but introducing only a minimum of spectral aberrations and thus allowing a potential speed-up of these types of simulations. First, the method is demonstrated on an artificial dimer system, where the effect on slope analysis, typically used to study spectral dynamics, is analysed. It is, furthermore, tested on the simulated two-dimensional infrared spectra in the amide I region of the protein lysozyme. The cross polarisation component is investigated, particularly sensitive to sampling noise, because it relies on cancelling of the dominant diagonal spectral contributions. In all these cases, the adaptive apodization scheme is found to give more accurate results than the commonly used lifetime apodization scheme and in most cases better than the gaussian apodization scheme.

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