Dr. Johannes Fuchs
Systematic Review of Experimental Studies on Data Glyphs
We systematically reviewed 64 user-study papers on data glyphs to help researchers and practitioners gain an informed understanding of tradeoffs in the glyph design space. The glyphs we consider are individual representations of multi-dimensional data points, often meant to be shown in small-multiple settings. Over the past 60 years many different glyph designs were proposed and many of these designs have been subjected to perceptual or comparative evaluations. Yet, a systematic overview of the types of glyphs and design variations tested, the tasks under which they were analyzed, or even the study goals and results does not yet exist.
In this paper we provide such an overview by systematically sampling and tabulating the literature on data glyph studies, listing their designs, questions, data, and tasks. In addition we present a concise overview of the types of glyphs and their design characteristics analyzed by researchers in the past, and a synthesis of the study results. Based on our meta analysis of all results we further contribute a set of design implications and a discussion on open research directions.
Data Glyph Designs: Selection of data glyph designs used in some of the quantitative experiments.
Evaluation of Alternative Glyph Designs for:
1. Time Series Data (received an Honorable Mention Award at CHI 2013)
Time series data is the basis for decision making in many different application domains---such as finance, network security, or traffic management. Detecting trends, spotting peaks, or investigating single points in time from a visual representation are daily analysis tasks of vital importance.
Yet, due to glyphs' power in presenting multiple time series for comparison, a multitude of designs have been proposed. Different visual variables such as length, color, or position can be used to encode two aspects of temporal data in one glyph: a) the location of a data point in time, and b) the quantitative data value. When confronted with the task of choosing an appropriate glyph design, a visualization designer or practitioner currently has little guidance on which encodings would be most appropriate for which tasks and on which visual features and factors influence people's perception of data encoded in glyphs.
In order to address this lack of guidance on the use of temporal glyphs, we ran a controlled experiment to compare four carefully selected glyphs using two different data densities.
Trend Detection Task: Four different glyph designs have the same underlying data. The glyph with the highest decrease over the whole displayed time period is artificially highlighted.
2. Multi Dimensional Data
We conducted three experiments to investigate the effects of contours on the detection of data similarity with star glyph variations. A star glyph is a small, compact, data graphic that represents a multi-dimensional data point. Star glyphs are often used in small-multiple settings, to represent data points in tables, on maps, or as overlays on other types of data graphics. In these settings, an important task is the visual comparison of the data points encoded in the star glyph, for example to find other similar data points or outliers. We hypothesized that for data comparisons, the overall shape of a star glyph---enhanced through contour lines--- would aid the viewer in making accurate similarity judgments. To test this hypothesis, we conducted three experiments. In our first experiment, we explored how the use of contours influenced how visualization experts and trained novices chose glyphs with similar data values.
Our results showed that glyphs without contours make the detection of data similarity easier. Given these results, we conducted a second study to understand intuitive notions of similarity. Star glyphs without contours most intuitively supported the detection of data similarity. In a third experiment, we tested the effect of star glyph reference structures (i.e., tickmarks and gridlines) on the detection of similarity. Surprisingly, our results show that adding reference structures does improve the correctness of similarity judgments for star glyphs with contours, but not for the standard star glyph.
As a result of these experiments, we conclude that the simple star glyph without contours performs best under several criteria, reinforcing its practice and popularity in the literature. Contours seem to enhance the detection of other types of similarity, e.g., shape similarity and are distracting when data similarity has to be judged.
Based on these findings we provide design considerations regarding the use of contours and reference structures on star glyphs.
Star Glyphs: Design space of the quantitative experiments.
Collaborative Data Analysis with Smart Tangible Devices
We present a tangible approach for exploring and comparing multi-dimensional data points collaboratively by combining Sifteo Cubes with glyph visualizations. Various interaction techniques like touching, shaking, moving or rotating the displays support the user in the analysis. Context dependent glyph-like visualization techniques make best use of the available screen space and cube arrangements. As a first proof of concept we apply our approach to real multi-dimensional datasets and show with a coherent use case how our techniques can facilitate the exploration and comparison of data points. Finally, further research directions are shown when combining Sifteo Cubes with glyphs and additional context information provided by multi-touch tables.
Sifteo Cubes in combination with a multi-touch table.
Leaf Glyph: Visualizing Multi-Dimensional Data with Environmental Cues
Paper (received the Student Best Paper Award at IVAPP 2015)
In exploratory data analysis, important analysis tasks include the assessment of similarity of data points, labeling of outliers, identifying and relating groups in data, and more generally, the detection of patterns. Specifically, for large data sets, such tasks may be effectively addressed by glyph-based visualizations. Appropriately defined glyph designs and layouts may represent collections of data to address these aforementioned tasks. Important problems in glyph visualization include the design of compact glyph representations, and a similarity- or structure-preserving 2D layout. Projection-based techniques are commonly used to generate layouts, but often suffer from over-plotting in 2D display space, which may hinder comparing and relating tasks.
We introduce a novel glyph design for visualizing multi-dimensional data based on an environmental metaphor. Motivated by the humans ability to visually discriminate natural shapes like trees in a forest, single flowers in a flower-bed, or leaves at shrubs, we design a leaf-shaped data glyph, where data controls main leaf properties including leaf morphology, leaf venation, and leaf boundary shape. We also define a custom visual aggregation scheme to scale the glyph for large numbers of data records. We show by example that our design is effectively interpretable to solve multivariate data analysis tasks, and provides effective data mapping. The design also provides an aesthetically pleasing appearance, which may help spark interest in data visualization by larger audiences, making it applicable e.g., in mass media.
Leaf Glyph: PCA projection of the IRIS and Seeds dataset.
Glyphs in Network Security Applications
In the area of network security, analysts have to deal with different kinds of data.
1. Time Series Data
While automatically monitoring the network for slow or failing components has become common practice, defining an acceptable state of the system is only possible to a very limited extent and thus exploratory analysis tasks by real human analysts complement the analysis process.
We have developed a tool called ClockView to enable visual support for monitoring large IP spaces. In particular, the presented system features 1) a scalable glyph representation in the style of a clock for giving an overview of the activity over time of thousands of hosts in the network, 2) subnet and port views for focusing the analysis to a particular subset of the data and 3) detailed pixel matrix visualizations for interpreting concrete traffic patterns. Furthermore, the tool's feedback loop, which is implemented through interaction capabilities, allows for retrieving new details, refocusing and enhancing of the overview.
ClockView: The tool is displayed on a high resolution Powerwall. Glyphs are used to visualize the daily traffic of single network devices.
2. Time Series Data & Hierarchical Data
Treemaps are a powerful method to visualize especially time-invariant hierarchical data. Most attention is drawn to rectangular treemaps, because their space-filling layouts provide good scalability with respect to the amount of data that can be displayed. Since circular treemaps sacrifice the space-filling property and since higher level circles only approximately match the aggregated size of their descendants, they are rarely used in practice. However, for drawing circular glyphs their shape preserving property can outweigh these disadvantages and facilitate comparative tasks within and across hierarchy levels.
The interactive ClockMap visualization effectively supports the user in exploring and finding patterns in hierarchical time-series data through drill-down, semantic zoom and details-on-demand. In this study, the technique's applicability is demonstrated on a real-world dataset about network traffic of a large computer network and its advantages and disadvantages are discussed in the context of alternative layouts.
ClockMap: Combining clock glyphs with circular treemaps.
3. Traceroutes and Anomalies
Routing in the Internet is vulnerable to attacks due to the insecure design of the border gateway protocol (BGP). One possible exploitation of this insecure design is the hijacking of IP blocks. Such hijacked IP blocks can then be used to conduct malicious activities from seemingly legitimate IP addresses. For this reason, we actively trace and monitor the routes to spam sources over several consecutive days after having received a spam message from such a source. To combine the strengths of human judgement and computational efficiency, we thus developed a novel visual analytics tool named VisTracer. This tool represents analysis results of our anomaly detection algorithms on large traceroute data sets with the help of several scalable representations to support the analyst to explore, identify and analyze suspicious events and their relations to malicious activities.
Graphical User Interface of VisTracer: (1) and (2) provide access to constraint filters and a table with observed anomalies. (3) Visual ASN Overview with occurred anomalies. A Feedback Panel is provided in (4) and access to individual traceroutes in (5) with map-based (6), glyph-based (7) and graph-based (8) visualizations.
Information Visualization I & II (Exercise)
The double course "Information Visualization I and II" gives an introduction to the field of Information Visualization. The course is composed of two parts, which are building on each other and which, given preconditions, can be taken together or independently.
The first part introduces basics of Information Visualization and can be taken by all students who have not previously heard basics of Information Visualization, e.g., as taught in the Analysis and Visualization lecture. The course will be closed by a written exam.
The second part introduces advanced techniques and problems in Information visualization. It is accommodated by a practical, project-oriented assignment and expected to be closed by a short oral colloquium.
In Particular, it covers foundations, relevant aspects of human perception, visualization design principles as well as an overview of visualization applications and interaction techniques. Information Visualization methods for particular data types, including 1, 2, and 3-dimensional, hierarchical, and spatial data will be considered. In addition, current research topics such as visualization for the masses, or streaming visualization will be discussed.
Analysis and Visualization (Exercise)
The students are taught elementary theoretical knowledge and get first practical experience in the data analysis domain. They obtain the ability to assess requirements and parameters for the application of fundamental analysis algorithms. Beyond that, students will practically apply and assess the results in an autonomous way.
In the visualization area, they are taught appropriate visual mappings for varying data types, and will apply them to form useful interactive visualization systems. The students will be enabled to judge design decisions considering properties of human perception and to develop and assess visualizations solutions.
D. Sacha, I. Boesecke, J. Fuchs and D. A. Keim.
Analytic Behavior and Trust Building in Visual Analytics.
Eurographics Conference on Visualization (EuroVis) - Short Papers, The Eurographics Association, DOI: 10.2312/eurovisshort.20161176, 2016.
J. Fuchs, P. Isenberg, A. Bezerianos and D. A. Keim.
A Systematic Review of Experimental Studies on Data Glyphs.
IEEE Transactions on Visualization and Computer Graphics, PP(99):1-1, DOI: 10.1109/TVCG.2016.2549018, 2016.
J. Fuchs, D. Jäckle, N. Weiler and T. Schreck.
Leaf Glyphs: Story Telling and Data Analysis Using Environmental Data Glyph Metaphors.
Computer Vision, Imaging and Computer Graphics Theory and Applications: 10th International Joint Conference, VISIGRAPP 2015, Berlin, Germany, March 11-14, 2015, Revised Selected Papers, Springer International Publishing, pages 123-143, DOI: 10.1007/978-3-319-29971-6_7, 2016.
D. Jäckle, J. Fuchs and D. A. Keim.
Star Glyph Insets for Overview Preservation of Multivariate Data.
IS&T Electronic Imaging Conference on Visualization and Data Analysis, 2016.
Glyph Design for Temporal and Multi-Dimensional Data : Design Considerations and Evaluation.
J. Fuchs, D. Jäckle, N. Weiler and T. Schreck.
Leaf Glyph - Visualizing Multi-Dimensional Data with Environmental Cues.
Proc. Int. Conference on Information Visualization Theory and Applications, 2015.
J. Fuchs, P. Isenberg, A. Bezerianos, F. Fischer and E. Bertini.
The Influence of Contour on Similarity Perception of Star Glyphs.
Visualization and Computer Graphics, IEEE Transactions on Visualization and Computer Graphics, IEEE, 20():2251 - 2260, DOI: 10.1109/TVCG.2014.2346426, 2014.
F. Fischer, J. Davey, J. Fuchs, O. Thonnard, J. Kohlhammer and D. A. Keim.
A Visual Analytics Field Experiment to Evaluate Alternative Visualizations for Cyber Security Applications.
Proc. EuroVA International Workshop on Visual Analytics, DOI: 10.2312/eurova.20141144, 2014.
J. Fuchs, R. Rädle, D. Sacha, F. Fischer and A. Stoffel.
Collaborative Data Analysis with Smart Tangible Devices.
SPIE 2013 Conference on Visualization and Data Analysis, IS&T/SPIE, pages 90170C, DOI: 10.1117/12.2040011, 2014.
F. Fischer, J. Fuchs, F. Mansmann and D. A. Keim.
BANKSAFE: Visual analytics for big data in large-scale computer networks.
Information Visualization, SAGE Publications, DOI: 10.1177/1473871613488572, 2013.
J. Fuchs, F. Fischer, F. Mansmann, E. Bertini and P. Isenberg.
Evaluation of Alternative Glyph Designs for Time Series Data in a Small Multiple Setting.
Proceedings of the Conference on Human Factors in Computing Systems (CHI), ACM, pages 3237-3246, DOI: 10.1145/2470654.2466443, 2013.
F. Fischer, J. Fuchs, F. Mansmann and D. A. Keim.
BANKSAFE: A Visual Situational Awareness Tool for Large-Scale Computer Networks (VAST Challenge 2012).
Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST Challenge 2012), pages 257-258, DOI: 10.1109/VAST.2012.6400528, 2012.
F. Fischer, J. Fuchs, P.-A. Vervier, F. Mansmann and O. Thonnard.
VisTracer: A Visual Analytics Tool to Investigate Routing Anomalies in Traceroutes.
Proceedings of the VizSec Symposium on Visualization for Cyber Security, ACM, DOI: 10.1145/2379690.2379701, 2012.
E. Biersack, Q. Jacquemart, F. Fischer, J. Fuchs, O. Thonnard, G. Theodoridis, D. Tzovaras and P.-A. Vervier.
Visual analytics for BGP monitoring and prefix hijacking identification.
Special Issue on Computer Network Visualization, IEEE Network Magazine, DOI: 10.1109/MNET.2012.6375891, 2012.
F. Fischer, J. Fuchs and F. Mansmann.
ClockMap: Enhancing Circular Treemaps with Temporal Glyphs for Time-Series Data.
Proceedings of the Eurographics Conference on Visualization (EuroVis 2012 Short Papers), pages 97-101, DOI: 10.2312/PE/EuroVisShort/EuroVisShort2012/097-101, 2012.
F. Wanner, J. Fuchs, D. Oelke and D. A. Keim.
Are my Children Old Enough to Read these Books? Age Suitability Analysis.
POLIBITS - Research journal on Computer science and computer engineering with applications, 2011.
C. Kintzel, J. Fuchs and F. Mansmann.
Monitoring Large IP Spaces with ClockView.
Proc. of Int. Symp. on Visualization for Cyber Security (VizSec), ACM, 2011.