29 research outputs found
Comparison of Node-Link and Hierarchical Edge Bundling Layouts: A User Study
Visually investigating large network-like structures is a challenging task.
Several approaches have been proposed in the past: node-link diagrams,
adjacency matrices, and, more recently, hierarchical edge bundles. We present a
recent experiment that compares the effectiveness of the classical node-link diagrams with the more recent hierarchical bundled edges. The users involved several computer science practitioners, the data ranged from graphs of several hundreds to several tens of hundreds of nodes, the tasks involved answering a number of structural overview as well as detailed questions involved system dependencies
Methods for visual mining of genomic and proteomic data atlases
<p>Abstract</p> <p>Background</p> <p>As the volume, complexity and diversity of the information that scientists work with on a daily basis continues to rise, so too does the requirement for new analytic software. The analytic software must solve the dichotomy that exists between the need to allow for a high level of scientific reasoning, and the requirement to have an intuitive and easy to use tool which does not require specialist, and often arduous, training to use. Information visualization provides a solution to this problem, as it allows for direct manipulation and interaction with diverse and complex data. The challenge addressing bioinformatics researches is how to apply this knowledge to data sets that are continually growing in a field that is rapidly changing.</p> <p>Results</p> <p>This paper discusses an approach to the development of visual mining tools capable of supporting the mining of massive data collections used in systems biology research, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. Example tools were developed which are designed to enable the exploration and analyses of both proteomics and genomics based atlases. These atlases represent large repositories of raw and processed experiment data generated to support the identification of biomarkers through mass spectrometry (the PeptideAtlas) and the genomic characterization of cancer (The Cancer Genome Atlas). Specifically the tools are designed to allow for: the visual mining of thousands of mass spectrometry experiments, to assist in designing informed targeted protein assays; and the interactive analysis of hundreds of genomes, to explore the variations across different cancer genomes and cancer types.</p> <p>Conclusions</p> <p>The mining of massive repositories of biological data requires the development of new tools and techniques. Visual exploration of the large-scale atlas data sets allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing linked task specific views that allow a user to start from points of interest (from diseases to single genes) enables targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as short a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools. These visualizations follow common metaphors where possible to assist users in understanding the displayed data. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of the large scale data being provided in atlases such as PeptideAtlas and The Cancer Genome Atlas.</p
CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling
Communication consists of both meta-information as well as content.
Currently, the automated analysis of such data often focuses either on the
network aspects via social network analysis or on the content, utilizing
methods from text-mining. However, the first category of approaches does not
leverage the rich content information, while the latter ignores the
conversation environment and the temporal evolution, as evident in the
meta-information. In contradiction to communication research, which stresses
the importance of a holistic approach, both aspects are rarely applied
simultaneously, and consequently, their combination has not yet received enough
attention in automated analysis systems. In this work, we aim to address this
challenge by discussing the difficulties and design decisions of such a path as
well as contribute CommAID, a blueprint for a holistic strategy to
communication analysis. It features an integrated visual analytics design to
analyze communication networks through dynamics modeling, semantic pattern
retrieval, and a user-adaptable and problem-specific machine learning-based
retrieval system. An interactive multi-level matrix-based visualization
facilitates a focused analysis of both network and content using inline visuals
supporting cross-checks and reducing context switches. We evaluate our approach
in both a case study and through formative evaluation with eight law
enforcement experts using a real-world communication corpus. Results show that
our solution surpasses existing techniques in terms of integration level and
applicability. With this contribution, we aim to pave the path for a more
holistic approach to communication analysis.Comment: 12 pages, 7 figures, Computer Graphics Forum 2021 (pre-peer reviewed
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