Towards Scalable Visual Exploration of Very Large RDF Graphs

In this paper, we outline our work on developing a disk-based infrastructure for efficient visualization and graph exploration operations over very large graphs. The proposed platform, called graphVizdb, is based on a novel technique for indexing and storing the graph. Particularly, the graph layout is indexed with a spatial data structure, i.e., an R-tree, and stored in a database. In runtime, user operations are translated into efficient spatial operations (i.e., window queries) in the backend.Comment: 12th Extended Semantic Web Conference (ESWC 2015

Mapping Topographic Structure in White Matter Pathways with Level Set Trees

Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that level set trees---which provide a concise representation of the hierarchical mode structure of probability density functions---offer a statistically-principled framework for visualizing and analyzing topography in fiber streamlines. Using diffusion spectrum imaging data collected on neurologically healthy controls (N=30), we mapped white matter pathways from the cortex into the striatum using a deterministic tractography algorithm that estimates fiber bundles as dimensionless streamlines. Level set trees were used for interactive exploration of patterns in the endpoint distributions of the mapped fiber tracks and an efficient segmentation of the tracks that has empirical accuracy comparable to standard nonparametric clustering methods. We show that level set trees can also be generalized to model pseudo-density functions in order to analyze a broader array of data types, including entire fiber streamlines. Finally, resampling methods show the reliability of the level set tree as a descriptive measure of topographic structure, illustrating its potential as a statistical descriptor in brain imaging analysis. These results highlight the broad applicability of level set trees for visualizing and analyzing high-dimensional data like fiber tractography output

Inviwo -- A Visualization System with Usage Abstraction Levels

The complexity of today's visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for instance by providing a data flow network editor. Unfortunately, these abstractions result in several issues, which need to be circumvented through an abstraction-centered system design. Often, a high level of abstraction hides low level details, which makes it difficult to directly access the underlying computing platform, which would be important to achieve an optimal performance. Therefore, we propose a layer structure developed for modern and sustainable visualization systems allowing developers to interact with all contained abstraction levels. We refer to this interaction capabilities as usage abstraction levels, since we target application developers with various levels of experience. We formulate the requirements for such a system, derive the desired architecture, and present how the concepts have been exemplary realized within the Inviwo visualization system. Furthermore, we address several specific challenges that arise during the realization of such a layered architecture, such as communication between different computing platforms, performance centered encapsulation, as well as layer-independent development by supporting cross layer documentation and debugging capabilities

VIOLA - A multi-purpose and web-based visualization tool for neuronal-network simulation output

Neuronal network models and corresponding computer simulations are invaluable tools to aid the interpretation of the relationship between neuron properties, connectivity and measured activity in cortical tissue. Spatiotemporal patterns of activity propagating across the cortical surface as observed experimentally can for example be described by neuronal network models with layered geometry and distance-dependent connectivity. The interpretation of the resulting stream of multi-modal and multi-dimensional simulation data calls for integrating interactive visualization steps into existing simulation-analysis workflows. Here, we present a set of interactive visualization concepts called views for the visual analysis of activity data in topological network models, and a corresponding reference implementation VIOLA (VIsualization Of Layer Activity). The software is a lightweight, open-source, web-based and platform-independent application combining and adapting modern interactive visualization paradigms, such as coordinated multiple views, for massively parallel neurophysiological data. For a use-case demonstration we consider spiking activity data of a two-population, layered point-neuron network model subject to a spatially confined excitation originating from an external population. With the multiple coordinated views, an explorative and qualitative assessment of the spatiotemporal features of neuronal activity can be performed upfront of a detailed quantitative data analysis of specific aspects of the data. Furthermore, ongoing efforts including the European Human Brain Project aim at providing online user portals for integrated model development, simulation, analysis and provenance tracking, wherein interactive visual analysis tools are one component. Browser-compatible, web-technology based solutions are therefore required. Within this scope, with VIOLA we provide a first prototype.Comment: 38 pages, 10 figures, 3 table

Facilitating insight into a simulation model using visualization and dynamic model previews

This paper shows how model simplification, by replacing iterative steps with unitary predictive equations, can enable dynamic interaction with a complex simulation process. Model previews extend the techniques of dynamic querying and query previews into the context of ad hoc simulation model exploration. A case study is presented within the domain of counter-current chromatography. The relatively novel method of insight evaluation was applied, given the exploratory nature of the task. The evaluation data show that the trade-off in accuracy is far outweighed by benefits of dynamic interaction. The number of insights gained using the enhanced interactive version of the computer model was more than six times higher than the number of insights gained using the basic version of the model. There was also a trend for dynamic interaction to facilitate insights of greater domain importance

Structuring visual exploratory analysis of skill demand

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on