Human-Centric Chronographics:Making Historical Time Memorable

A series of experiments is described, evaluating user recall of visualisations of historical chronology. Such visualisations are widely created but have not hitherto been evaluated. Users were tested on their ability to learn a sequence of historical events presented in a virtual environment (VE) fly-through visualisation, compared with the learning of equivalent material in other formats that are sequential but lack the 3D spatial aspect. Memorability is a particularly important function of visualisation in education. The measures used during evaluation are enumerated and discussed. The majority of the experiments reported compared three conditions, one using a virtual environment visualisation with a significant spatial element, one using a serial on-screen presentation in PowerPoint, and one using serial presentation on paper. Some aspects were trialled with groups having contrasting prior experience of computers, in the UK and Ukraine. Evidence suggests that a more complex environment including animations and sounds or music, intended to engage users and reinforce memorability, were in fact distracting. Findings are reported in relation to the age of the participants, suggesting that children at 11–14 years benefit less from, or are even disadvantaged by, VE visualisations when compared with 7–9 year olds or undergraduates. Finally, results suggest that VE visualisations offering a ‘landscape’ of information are more memorable than those based on a linear model. Keywords: timeline, chronographic

A framework for using self-organising maps to analyse spatiotemporal patterns, exemplified by analysis of mobile phone usage

We suggest a visual analytics framework for the exploration and analysis of spatially and temporally referenced values of numeric attributes. The framework supports two complementary perspectives on spatio-temporal data: as a temporal sequence of spatial distributions of attribute values (called spatial situations) and as a set of spatially referenced time series of attribute values representing local temporal variations. To handle a large amount of data, we use the self-organising map (SOM) method, which groups objects and arranges them according to similarity of relevant data features. We apply the SOM approach to spatial situations and to local temporal variations and obtain two types of SOM outcomes, called space-in-time SOM and time-in-space SOM, respectively. The examination and interpretation of both types of SOM outcomes are supported by appropriate visualisation and interaction techniques. This article describes the use of the framework by an example scenario of data analysis. We also discuss how the framework can be extended from supporting explorative analysis to building predictive models of the spatio-temporal variation of attribute values. We apply our approach to phone call data showing its usefulness in real-world analytic scenarios

Computing and visually analyzing mutual information in molecular co-evolution

<p>Abstract</p> <p>Background</p> <p>Selective pressure in molecular evolution leads to uneven distributions of amino acids and nucleotides. In fact one observes correlations among such constituents due to a large number of biophysical mechanisms (folding properties, electrostatics, ...). To quantify these correlations the mutual information -after proper normalization - has proven most effective. The challenge is to navigate the large amount of data, which in a study for a typical protein cannot simply be plotted.</p> <p>Results</p> <p>To visually analyze mutual information we developed a matrix visualization tool that allows different views on the mutual information matrix: filtering, sorting, and weighting are among them. The user can interactively navigate a huge matrix in real-time and search e.g., for patterns and unusual high or low values. A computation of the mutual information matrix for a sequence alignment in FASTA-format is possible. The respective stand-alone program computes in addition proper normalizations for a null model of neutral evolution and maps the mutual information to <it>Z</it>-scores with respect to the null model.</p> <p>Conclusions</p> <p>The new tool allows to compute and visually analyze sequence data for possible co-evolutionary signals. The tool has already been successfully employed in evolutionary studies on HIV1 protease and acetylcholinesterase. The functionality of the tool was defined by users using the tool in real-world research. The software can also be used for visual analysis of other matrix-like data, such as information obtained by DNA microarray experiments. The package is platform-independently implemented in <monospace>Java</monospace> and free for academic use under a GPL license.</p

Visual Analytics Approaches for Descriptor Space Comparison and the Exploration of Time Dependent Data

Modern technologies allow us to collect and store increasing amounts of data. However, their analysis is often difficult. For that reason, Visual Analytics combines data mining and visualization techniques to explore and an- alyze large amounts of complex data. Visual Analytics approaches exist for various problems and applications, but all share the idea of a tight combination of visualization and automatic analysis. Their respective implemen- tations are highly specialized on the given data and the analytical task. In this thesis I present new approaches for two specific topics, visual descriptor space comparison and the analysis of time series. Visual descriptor space comparison enables the user to analyze different representations of complex datasets e.g., phylogenetic trees or chemical compounds. I propose approaches for data sets with hierarchic or unknown structure, each combining an automatic analysis with interactive visualization. For hierarchically organized data, I suggest a novel similarity score embedded in an interactive analysis framework linking different views, each specialized on a particular analytical tasks. This analysis framework is evaluated in cooperation with biologists in the area of phylogenetic research. To extend the scalability of my approach, I introduce CloudTrees, a new vi- sualization technique for the comparison of large trees with thousands of leaves. It reduces overplotting problems by ensuring the visibility of small but important details like high scoring subtrees. For the comparison of data with unknown structure, I assess several state of the art projection quality measures to analyze their capability for descriptor comparison. For the creation of appropriate ground truth test data. I suggest an interactive tool called PCDC for the controlled creation of high dimensional data with different properties like data distribution or number and size of contained clusters. For the visual comparison of unknown structured data, I introduce a technique which bases on the comparison of two dimensional projections of the descriptors using a two dimensional colormap. I present the approach for scatterplots and extended it to Self- Organizing Maps (SOMs) including reliability encoding. I embed the automatic and visual comparison in an interactive analysis pipeline, which automatically calculates a set of representative descriptors out of a larger collection of descriptors. For a deeper analysis of the proposed result and the underlying characteristics of the input data, the analyst can follow each step of the pipeline. The approach is applied to a large set of chemical data in a high throughput screening analysis scenario. For the analysis of time dependent, categorical data I propose a new approach called Time Parallel Sets (TIPS). It focuses on the analysis of group changes of objects in large datasets. Different automatic algorithms identify and select potentially interesting points in time for a detailed analysis. The user can interactively track groups or single objects, add or remove selected points in time or change parameters of the detection algorithms according to the analytical goal. The approach is applied to two scenarios: Emergency evacuation of buildings and tracking of mobile phone calls over long time periods. Large time series can be compressed by transforming them into sequences of symbols whereas each symbol represents a set of similar subsequences in time. For these time sequences, I propose new visual-analytical tools, starting with an interactive, semi-automatic definition of symbol similarity. Based on this, the sequences are visualized using different linked views, each specialized on other analytical problems. As an example usecase, a financial dataset containing the risk estimations and return values of 60 companies over 500 days is analyzed

Ein flexibles System für die explorative visuelle Sequenzanalyse

Visual Analytics ist eine junge Disziplin, die automatische Datenanalyse mit Visualisierung und Mensch-Computer-Interaktion verbindet. Diese Kombination hat sich in vielen Fällen bereits als besonders effektiv bei der explorativen Analyse großer und komplexer Daten erwiesen. Ein Beispiel hierfür sind große Mengen von Sequenzdaten, also eine lineare Folge von Objekten. Diese treten in vielen Arbeitsgebieten wie z.B. der Biologie als Folge von Aminosäuren, digitaler Bibliotheken als Folge von Zeichen und Worten sowie im Finanzsektor als Folge von Kurswerten auf. Wir stellen in dieser Arbeit ein System zur explorativen visuellen Analyse diskretisierter Sequenzen vor. Die neuen Werkzeuge sind in eine Pipeline zur Analyse großer Datenmengen eingebunden. Wir bieten dem Benutzer viele interaktive Möglichkeiten, Bestandteile der Pipeline zu kombinieren und Optionen der verwendeten Algorithmen zu beeinflussen, um der Diversität der Daten gerecht zu werden. Diese umfassenden Möglichkeiten, kombiniert mit entsprechenden Visualisierungen, führen zu einem Verständnis des Datensatzes und den darin enthaltenen Informationen durch den Analysten. Diese Erkenntnisse können dann sowohl als Grundlage von Entscheidungen als auch als Ausgangspunkt für weitere Analyseschritte dienen

Ein flexibles System für die explorative visuelle Sequenzanalyse

Visual Analytics ist eine junge Disziplin, die automatische Datenanalyse mit Visualisierung und Mensch-Computer-Interaktion verbindet. Diese Kombination hat sich in vielen Fällen bereits als besonders effektiv bei der explorativen Analyse großer und komplexer Daten erwiesen. Ein Beispiel hierfür sind große Mengen von Sequenzdaten, also eine lineare Folge von Objekten. Diese treten in vielen Arbeitsgebieten wie z.B. der Biologie als Folge von Aminosäuren, digitaler Bibliotheken als Folge von Zeichen und Worten sowie im Finanzsektor als Folge von Kurswerten auf. Wir stellen in dieser Arbeit ein System zur explorativen visuellen Analyse diskretisierter Sequenzen vor. Die neuen Werkzeuge sind in eine Pipeline zur Analyse großer Datenmengen eingebunden. Wir bieten dem Benutzer viele interaktive Möglichkeiten, Bestandteile der Pipeline zu kombinieren und Optionen der verwendeten Algorithmen zu beeinflussen, um der Diversität der Daten gerecht zu werden. Diese umfassenden Möglichkeiten, kombiniert mit entsprechenden Visualisierungen, führen zu einem Verständnis des Datensatzes und den darin enthaltenen Informationen durch den Analysten. Diese Erkenntnisse können dann sowohl als Grundlage von Entscheidungen als auch als Ausgangspunkt für weitere Analyseschritte dienen

Techniques for precision-based visual analysis of projected data

The analysis of high-dimensional data is an important, yet inherently difficult problem. Projection techniques such as Principal Component Analysis, Multi-dimensional Scaling and Self-Organizing Map can be used to map high-dimensional data to 2D display space. However, projections typically incur a loss in information. Often, uncertainty exists regarding the precision of the projection as compared with its original data characteristics. While the output quality of these projection techniques can be discussed in terms of aggregate numeric error values, visualization is often helpful for better understanding the projection results. We address the visual assessment of projection precision by an approach integrating an appropriately designed projection precision measure directly into the projection visualization. To this end, a flexible projection precision measure is defined that allows the user to balance the degree of locality at which the measure is evaluated. Several visual mappings are designed for integrating the precision measure into the projection visualization at various levels of abstraction. The techniques are implemented in an interactive system, including methods supporting the user in finding appropriate settings of relevant parameters. We demonstrate the usefulness of the approach for visual analysis of classified and unclassified high-dimensional data sets. We show how our interactive precision quality visualization system helps to examine the preservation of original data properties in projected space

Typology of Uncertainty in Static Geolocated Graphs for Visualization

Static geolocated graphs have nodes connected by edges that can have geographic location and associated attributes. This article proposes a typology of uncertainty in static geolocated graphs, which can affect the existence, location, attributes, or grouping of nodes and edges. The authors also summarize available techniques for visualizing such uncertainty

Techniques for Precision-Based Visual Analysis of Projected Data

The analysis of high-dimensional data is an important, yet inherently difficult problem. Projection techniques such as PCA, MDS, and SOM can be used to map high-dimensional data to 2D display space. However, projections typically incur a loss in information. Often uncertainty exists regarding the precision of the projection as compared with its original data characteristics. While the output quality of these projection techniques can be discussed in terms of algorithmic assessment, visualization is often helpful for better understanding the results. We address the visual assessment of projection precision by an approach integrating an appropriately designed projection precision measure directly into the projection visualization. To this end, a flexible projection precision measure is defined that allows the user to balance the degree of locality at which the measure is evaluated. Several visual mappings are designed for integrating the precision measure into the projection visualization at various levels of abstraction. The techniques are implemented in a fully interactive system which is practically applied on several data sets. We demonstrate the usefulness of the approach for visual analysis of classified and clustered high-dimensional data sets. We thereby show how our novel interactive precision quality visualization system helps to examine preservation of closeness of the data in original space into the low-dimensional space