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Bernard, Jürgen; Zeppelzauer, Matthias; Sedlmair, Michael; Aigner, Wolfgang

A Unified Process for Visual-Interactive Labeling

2017

Sedlmaier, Michael (Ed.) et al.: EuroVA 2017 : EuroVis Workshop on Visual Analytics. Goslar: Eurographics Association, 2017, pp. 73-77

International EuroVis Workshop on Visual Analytics (EuroVA) <8, 2017, Barcelona, Spain>

Assigning labels to data instances is a prerequisite for many machine learning tasks. Similarly, labeling is applied in visualinteractive analysis approaches. However, the strategies for creating labels often differ in the two fields. In this paper, we study the process of labeling data instances with the user in the loop, from both the machine learning and visual-interactive perspective. Based on a review of differences and commonalities, we propose the 'Visual-Interactive Labeling' (VIAL) process, conflating the strengths of both. We describe the six major steps of the process and highlight their related challenges.

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Bernard, Jürgen; Vögele, Anna; Klein, Reinhard; Fellner, Dieter W.

Approaches and Challenges in the Visual-interactive Comparison of Human Motion Data

2017

Linsen, Lars (Ed.) et al.: IVAPP 2017. Proceedings : 8th International Conference on Information Visualization Theory and Applications (VISIGRAPP 2017 Volume 3). SciTePress, 2017, pp. 217-224

International Conference on Information Visualization Theory and Applications (IVAPP) <8, 2017, Porto, Portugal>

Many analysis goals involving human motion capture (MoCap) data require the comparison of motion patterns. Pioneer works in visual analytics recently recognized visual comparison as substantial for visual-interactive analysis. This work reflects the design space for visual-interactive systems facilitating the visual comparison of human MoCap data, and presents a taxonomy comprising three primary factors, following the general visual analytics process: algorithmic models, visualizations for motion comparison, and back propagation of user feedback. Based on a literature review, relevant visual comparison approaches are discussed. We outline remaining challenges and inspiring works on MoCap data, information visualization, and visual analytics.

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Bernard, Jürgen; Dobermann, Eduard; Sedlmair, Michael; Fellner, Dieter W.

Combining Cluster and Outlier Analysis with Visual Analytics

2017

Sedlmaier, Michael (Ed.) et al.: EuroVA 2017 : EuroVis Workshop on Visual Analytics. Goslar: Eurographics Association, 2017, pp. 19-23

International EuroVis Workshop on Visual Analytics (EuroVA) <8, 2017, Barcelona, Spain>

Cluster and outlier analysis are two important tasks. Due to their nature these tasks seem to be opposed to each other, i.e., data objects either belong to a cluster structure or a sparsely populated outlier region. In this work, we present a visual analytics tool that allows the combined analysis of clusters and outliers. Users can add multiple clustering and outlier analysis algorithms, compare results visually, and combine the algorithms' results. The usefulness of the combined analysis is demonstrated using the example of labeling unknown data sets. The usage scenario also shows that identified clusters and outliers can share joint areas of the data space.

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Adam, Meike; Tennstedt, Pierre; Lanwehr, Dominik; Tilki, Derya; Steuber, Thomas; Beyer, Burkhard; Thederan, Imke; Heinzer, Hans; Haese, Alexander; Salomon, Georg; Budäus, Lars; Michl, Uwe; Pehrke, Dirk; Stattin, Pär; Bernard, Jürgen; Klaus, Bernd; Pompe, Raisa S.; Petersen, Cordula; Huland, Hartwig; Graefen, Markus; Schwarz, Rudolf; Huber, Wolfgang; Loeb, Stacy; Schlomm, Thorsten

Functional Outcomes and Quality of Life After Radical Prostatectomy Only Versus a Combination of Prostatectomy with Radiation and Hormonal Therapy

2017

European Urology, Vol.71 (2017), 3, pp. 330-336

Background: While the optimal use and timing of secondary therapy after radical prostatectomy (RP) remain controversial, there are limited data on patient-reported outcomes following multimodal therapy. Objective: To assess the impact of additional radiation therapy (RT) and/or androgen deprivation therapy (ADT) on urinary continence, potency, and quality of life (QoL) after RP. Design, setting, and participants: Among 13 150 men who underwent RP from 1992 to 2013, 905 received RP + RT, 407 RP + ADT and 688 RP + RT + ADT. Outcome measurements and statistical analyses: Urinary function, sexual function, and overall QoL were evaluated annually using self-administered validated questionnaires. Propensity score-matched and bootstrap analyses were performed, and the distributions for all functional outcomes were analyzed as a function of time after RP. Results and limitations: Patients who received RP + RT had a 4% higher overall incontinence rate 3 yr after surgery, and 1% higher rate for severe incontinence (>3 pads/24 h) compared to matched RP-only patients. ADT further increased the overall and severe incontinence rates by 4% and 3%, respectively, compared to matched RP + RT patients. RP + RT was associated with an 18% lower rate of potency compared to RP alone, while RP + RT + ADT was associated with a further 17% reduction compared to RP + RT. Additional RT reduced QoL by 10% and additional ADT by a further 12% compared to RP only and RP + RT, respectively. The timing of RT after RP had no influence on continence, but adjuvant compared to salvage RT was associated with significantly lower potency (37% vs 45%), but higher QoL (60% vs 56%). Limitations of our study include the observational study design and potential for selection bias in the treatments received. Conclusions: Secondary RT and ADT after RP have an additive negative influence on urinary function, potency, and QoL. Patients with high-risk disease should be counseled before RP on the potential net impairment of functional outcomes due to multimodal treatment. Patient summary: Men with high-risk disease choosing surgery upfront should be counseled on the potential need for additional radiation and or androgen deprivation, and the potential net impairment of functional outcomes arising from multimodal treatment.

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Ritter, Christian; Kuijper, Arjan (Betreuer); Bernard, Jürgen (Betreuer)

Personalized Music Classification and Feature Creation based on Visual-Interactive Learning

2017

Darmstadt, TU, Bachelor Thesis, 2017

I present an approach for personalized music classification and feature generation. Currently existing approaches cover general models that match the common taste of music and use (small) sets of pre-defined features. Allowing a user to interactively build a music classification model is a complex task. In this work, I introduce a two-level approach that utilizes Visual Analytics to solve that task. At high-level granularity, a user can interactively classify music in a visual interface. The system supports the user with multiple views that grant model insights and information about the feature space. Furthermore, the user can freely define classes and assign them to songs of his collection. At low-level granularity, a user is able to create features matching his task by defining important audio patterns. A similarity over the song is calculated from each pattern and then used to create features that depend on the preferences of the user. The approach I contribute allows classification of music (or, in general, multivariate time series) for various tasks with features created at run-time. Thus, it makes classification interactive and applicable to multiple tasks. Several evaluation techniques showed the usefulness of the approach in different scenarios and the effectiveness of personalized features.

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Ruppert, Tobias; Bannach, Andreas; Bernard, Jürgen; Lokanc, Martin; Kohlhammer, Jörn

Visual Access to Performance Indicators in the Mining Sector

2017

Kozlíková, Barbora (Ed.) et al.: Eurographics / IEEE VGTC Conference on Visualization (EuroVis) - Short Papers. Goslar: Eurographics Association, 2017, pp. 157-161

Eurographics / IEEE VGTC Conference on Visualization (EuroVis) <19, 2017, Barcelona, Spain>

We introduce a visualization system that provides visual interactive access to information relevant for decision making in the mining sector. The mining sector is one of the most important industries in developing countries, especially in Africa. Stakeholders like governments, investors, and the civil society play an important role in the growth of the mining sector. They are interested in information reviewing individual country performances towards mining. The Mining Investment and Governance Review (MInGov) dataset explicitly addresses this issue. However, the complex data structure introduces challenges for the intuitive and easy understanding of the information. Together with mining sector experts, we conducted a design study with the goal to provide visual interactive access to investment- and policy-related information. We report on a domain characterization of the MInGov dataset, its potential users, and their tasks. Based on this analysis, we design a visualization system that supports mining-related decision making. Finally, we evaluate the visualization system in a user workshop with domain experts.

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Ruppert, Tobias; Staab, Michael; Bannach, Andreas; Lücke-Tieke, Hendrik; Bernard, Jürgen; Kuijper, Arjan; Kohlhammer, Jörn

Visual Interactive Creation and Validation of Text Clustering Workflows to Explore Document Collections

2017

Wischgoll, Thomas (Ed.) et al.: Visualization and Data Analysis 2017. Springfield: IS&T, 2017. (Electronic Imaging), pp. 46-57

Visualization and Data Analysis (VDA) <2017, Burlingame, CA, USA>

The exploration of text document collections is a complex and cumbersome task. Clustering techniques can help to group documents based on their content for the generation of overviews. However, the underlying clustering workflows comprising preprocessing, feature selection, clustering algorithm selection and parameterization offer several degrees of freedom. Since no "best" clustering workflow exists, users have to evaluate clustering results based on the data and analysis tasks at hand. In our approach, we present an interactive system for the creation and validation of text clustering workflows with the goal to explore document collections. The system allows users to control every step of the text clustering workflow. First, users are supported in the feature selection process via feature selection metrics-based feature ranking and linguistic filtering (e.g., part-of-speech filtering). Second, users can choose between different clustering methods and their parameterizations. Third, the clustering results can be explored based on the cluster content (documents and relevant feature terms), and cluster quality measures. Fourth, the results of different clusterings can be compared, and frequent document subsets in clusters can be identified. We validate the usefulness of the system with a usage scenario describing how users can explore document collections in a visual and interactive way.

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Sessler, David; Kuijper, Arjan (Betreuer); Bernard, Jürgen (Betreuer)

Visual-Interactive Learning of Time Series Similarity

2017

Darmstadt, TU, Master Thesis, 2017

Similarity is important for the applicability of a series of data analysis tasks. Pattern recognition, clustering and nearest neighbor search require a meaningful similarity function for their functionality. In this work, we consider a similarity function to be meaningful, if it reflects the similarity notion in the minds of the users. Therefore, the design of a similarity function has to incorporate and reflect user's preferences. In this work we focus on the similarity for time-oriented data. The definition of similarity functions for this data type requires a cascade of routines, including preprocessing steps, descriptors, normalization steps, and distance measures. Referring to this cascade, manually choosing appropriate routines matching the user's expectations of time series similarity is a tedious process. We present a visual-interactive approach that identifies meaningful similarity functions for time-oriented data automatically. The core principle is to learn from user-defined labels about the similarity of pairs of time series. Automatic choice of fitting routines allows to match the similarity notion of the users at run time. We implement a labeling interface for pairwise time series, including active learning support to enhance the learning process. Different views allow the analysis of the learning process of similarity for time series data. A list-based ranking interface provides detailed information on the best performing similarity functions. Filtering interfaces allow for detailed analysis of the applicability of routines included in the similarity functions. Furthermore, they provide drill-down functionality that can be used to experiment with different sets of similarity functions, in order to increase the robustness of the prediction. Nearest neighbor search closes the feedback loop and enables the users to validate, if the defined similarity function complies with their notion of similarity. In addition, we demonstrate the applicability of the approach in case studies based on different pre-defined notions of similarity used for labeling. Finally, we evaluate our approach to determine which factors influence the prediction accuracy. In conclusion, our approach extends the classical user-centered and iterative design process to an online learning process that defines similarity functions based on user feedback. We report on an increase in efficiency from a tedious design process for similarity functions, down to a process that only takes minutes of expert time.

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Bernard, Jürgen; Dobermann, Eduard; Vögele, Anna; Krüger, Björn; Kohlhammer, Jörn; Fellner, Dieter W.

Visual-Interactive Semi-Supervised Labeling of Human Motion Capture Data

2017

Wischgoll, Thomas (Ed.) et al.: Visualization and Data Analysis 2017. Springfield: IS&T, 2017. (Electronic Imaging), pp. 34-45

Visualization and Data Analysis (VDA) <2017, Burlingame, CA, USA>

The characterization and abstraction of large multivariate time series data often poses challenges with respect to effectiveness or efficiency. Using the example of human motion capture data challenges exist in creating compact solutions that still reflect semantics and kinematics in a meaningful way. We present a visual-interactive approach for the semi-supervised labeling of human motion capture data. Users are enabled to assign labels to the data which can subsequently be used to represent the multivariate time series as sequences of motion classes. The approach combines multiple views supporting the user in the visual-interactive labeling process. Visual guidance concepts further ease the labeling process by propagating the results of supportive algorithmic models. The abstraction of motion capture data to sequences of event intervals allows overview and detail-on-demand visualizations even for large and heterogeneous data collections. The guided selection of candidate data for the extension and improvement of the labeling closes the feedback loop of the semi-supervised workflow. We demonstrate the effectiveness and the efficiency of the approach in two usage scenarios, taking visual-interactive learning and human motion synthesis as examples.

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Bernard, Jürgen; Ritter, Christian; Sessler, David; Zeppelzauer, Matthias; Kohlhammer, Jörn; Fellner, Dieter W.

Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis

2017

Linsen, Lars (Ed.) et al.: IVAPP 2017. Proceedings : 8th International Conference on Information Visualization Theory and Applications (VISIGRAPP 2017 Volume 3). SciTePress, 2017, pp. 75-87

International Conference on Information Visualization Theory and Applications (IVAPP) <8, 2017, Porto, Portugal>

The definition of similarity is a key prerequisite when analyzing complex data types in data mining, information retrieval, or machine learning. However, the meaningful definition is often hampered by the complexity of data objects and particularly by different notions of subjective similarity latent in targeted user groups. Taking the example of soccer players, we present a visual-interactive system that learns users' mental models of similarity. In a visual-interactive interface, users are able to label pairs of soccer players with respect to their subjective notion of similarity. Our proposed similarity model automatically learns the respective concept of similarity using an active learning strategy. A visual-interactive retrieval technique is provided to validate the model and to execute downstream retrieval tasks for soccer player analysis. The applicability of the approach is demonstrated in different evaluation strategies, including usage scenarions and cross-validation tests.

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Bernard, Jürgen

Explorative Suche in zeitbasierten Primärdaten

2016

Hölldobler, Steffen (Ed.) et al.: Ausgezeichnete Informatikdissertationen 2015. Bonn: Köllen, 2016. (GI-Edition - Lecture Notes in Informatics (LNI) D-16), pp. 11-20

Die Ära des Big Data birgt gewaltige Potenziale für die datenzentrierte Forschung, denen Herausforderungen wie die Größe, die Qualität oder temporale Aspekte der Daten gegenüberstehen. Für die explorative Suche nach unerforschtem Wissen in komplexen Daten benötigen Domänenexperten effektive Analysetechniken und -systeme. Im Design dieser Systeme lassen sich die Kompetenzen von Data Scientists mit denen der Domänenexperten vereinen. Am Beispiel von zeitbasierten Primärdaten präsentiere ich in meiner Dissertation Konzepte, Richtlinien, Techniken und Systeme für die explorative Suche zur Unterstützung der datenzentrierten Forschung. Dabei verfolge ich in einem Visual-Analytics-Ansatz die strikte Kopplung von visuell-interaktiven Benutzerschnittstellen mit algorithmischen Modellen zur Datenanalyse. Beim Design von explorativen Suchsystemen ermögliche ich den Vergleich und die Auswahl von Modellen, unter Einbezug von Domänenexperten.

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Ruddle, Roy A.; Bernard, Jürgen; May, Thorsten; Lücke-Tieke, Hendrik; Kohlhammer, Jörn

Methods and a Research Agenda for the Evaluation of Event Sequence Visualization Techniques

2016

IEEE Computer Society Visualization and Graphics Technical Committee (VGTC): IEEE VIS 2016 Workshop on Temporal and Sequential Event Analysis : The Event Event: Temporal & Sequential Event Analysis [online]. [cited 01 February 2017] Available from: [http://eventevent.github.io/], 2016, 4 p.

IEEE VIS 2016 Workshop on Temporal & Sequential Event Analysis <2016, Baltimore, USA>

The present paper asks how can visualization help data scientists make sense of event sequences, and makes three main contributions. The first is a research agenda, which we divide into methods for presentation, interaction & computation, and scale-up. Second, we introduce the concept of Event Maps to help with scale-up, and illustrate coarse-, medium- and fine-grained Event Maps with electronic health record (EHR) data for prostate cancer. Third, in an experiment we investigated participants' ability to judge the similarity of event sequences. Contrary to previous research into categorical data, color and shape were better than position for encoding event type. However, even with simple sequences (5 events of 3 types in the target sequence), participants only got 88% correct despite averaging 7.4 seconds to respond. This indicates that simple visualization techniques are not effective.

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Ruppert, Tobias; Bannach, Andreas; Bernard, Jürgen; Lücke-Tieke, Hendrik; Ulmer, Alex; Kohlhammer, Jörn

Supporting Collaborative Political Decision Making - An Interactive Policy Process Visualization System

2016

Kerren, Andreas (Ed.) et al.: Proceedings of the 9th International Symposium on Visual Information Communication and Interaction : INCI 2016 [online]. ACM, 2016, 8 p.

International Symposium on Visual Information Communication and Interaction (VINCI 2016) < 9, 2016, Dallas, Texas>

The process of political decision making is often complex and tedious. The policy process consists of multiple steps, most of them are highly iterative. In addition, different stakeholder groups are involved in political decision making and contribute to the process. A series of textual documents accompanies the process. Examples are official documents, discussions, scientific reports, external reviews, newspaper articles, or economic white papers. Experts from the politi- cal domain report that this plethora of textual documents often exceeds their ability to keep track of the entire policy process. We present PolicyLine, a visualization system that supports different stakeholder groups in overview-and-detail tasks for large sets of textual documents in the political decision making process. In a longitudinal design study conducted together with domain experts in political decision making, we identfied missing analytical functionality on the basis of a problem and domain characterization. In an iterative design phase, we created PolicyLine in close collaboration with the domain experts. Finally, we present the results of three evaluation rounds, and reect on our collaborative visualization system.

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Sessler, David; Spott, Martin; Nauck, Detlef; Harmer, William; Kohlhammer, Jörn; Bernard, Jürgen

Towards Combining Attribute-Based and Time Series-Based Visual Querying

2016

Isenberg, Tobias (Ed.) et al.: EuroVis 2016. Eurographics / IEEE Symposium on Visualization 2016: Posters. Goslar: Eurographics Association, 2016, pp. 73-75

Eurographics Conference on Visualization (EuroVis) <18, 2016, Groningen, The Netherlands>

We present a concept for the visual-interactive definition of meaningful subsets in data sets comprising multivariate attributes and time series data. Based on a generalization of requirements of a real-world user group, we propose a three-stage approach, combining visual-interactive querying, query filter analysis, and result exploration. The approach includes several design parameters that can easily be adapted in future design studies for alternative applications.

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Loeb, Stacy; Adam, Meike; Tennstedt, Pierre; Huber, Wolfgang; Bernard, Jürgen; Tilki, Derya; Graefen, Markus; Huland, Hartwig; Schlomm, Thorsten

Toxicity in a multimodality approach of radical prostatectomy with radiation and hormonal therapy

2016

Journal of Clinical Oncology, Vol.34 (2016), 2, p. 107

Genitourinary Cancers Symposium < 2016, San Francisco, CA>

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Bernard, Jürgen; Sessler, David; Steiger, Martin; Spott, Martin; Kohlhammer, Jörn

Visual-Interactive Exploration of Relations Between Time-Oriented Data and Multivariate Data

2016

Andrienko, Natalia (Ed.) et al.: EuroVA 2016 : EuroVis Workshop on Visual Analytics. Goslar: Eurographics Association, 2016, pp. 49-53

International EuroVis Workshop on Visual Analytics (EuroVA) <7, 2016, Groningen, The Netherlands>

The analysis of large, multivariate data sets is challenging, especially when some of these data objects are timeoriented. Exploring relationships between multivariate and temporal information, e.g., to identify patterns that support decision making is an important industrial analysis task. The target group of this design study are data analysts aiming at detecting fault patterns in a telecommunications network in order to spend maintenance budget more effectively. We present a visual analytics tool that provides overviews of multivariate data sets and associated time series. Users can select data subsets of interest in both attribute data and clustered time series data. Linked views consequently support the identification of relations between the two spaces. To ensure usefulness, the tool was designed in an iterative way, based on a careful characterization of the data, users, and tasks. A usage scenario demonstrates the applicability of the approach.

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Bernard, Jürgen; Dobermann, Eduard; Bögl, Markus; Röhlig, Martin; Vögele, Anna; Kohlhammer, Jörn

Visual-Interactive Segmentation of Multivariate Time Series

2016

Andrienko, Natalia (Ed.) et al.: EuroVA 2016 : EuroVis Workshop on Visual Analytics. Goslar: Eurographics Association, 2016, p. 31-35

International EuroVis Workshop on Visual Analytics (EuroVA) <7, 2016, Groningen, The Netherlands>

Choosing appropriate time series segmentation algorithms and relevant parameter values is a challenging problem. In order to choose meaningful candidates it is important that different segmentation results are comparable. We propose a Visual Analytics (VA) approach to address these challenges in the scope of human motion capture data, a special type of multivariate time series data. In our prototype, users can interactively select from a rich set of segmentation algorithm candidates. In an overview visualization, the results of these segmentations can be compared and adjusted with regard to visualizations of raw data. A similarity-preserving colormap further facilitates visual comparison and labeling of segments. We present our prototype and demonstrate how it can ease the choice of winning candidates from a set of results for the segmentation of human motion capture data.

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Bernard, Jürgen; Steiger, Martin; Mittelstädt, Sebastian; Thum, Simon; Keim, Daniel A.; Kohlhammer, Jörn

A Survey and Task-Based Quality Assessment of Static 2D Colormaps

2015

Kao, David L. (Ed.) et al.: Visualization and Data Analysis 2015. SPIE Press, 2015. (Proceedings of SPIE 9397), pp. 93970M-1 - 93970M-16

IS&T/SPIE Conference on Visualization and Data Analysis (VDA) <15, 2015, San Francisco, CA, USA>

Color is one of the most important visual variables since it can be combined with any other visual mapping to encode information without using additional space on the display. Encoding one or two dimensions with color is widely explored and discussed in the field. Also mapping multi-dimensional data to color is applied in a vast number of applications, either to indicate similar, or to discriminate between different elements or (multi-dimensional) structures on the screen. A variety of 2D colormaps exists in literature, covering a large variance with respect to different perceptual aspects. Many of the colormaps have a different perspective on the underlying data structure as a consequence of the various analysis tasks that exist for multivariate data. Thus, a large design space for 2D colormaps exists which makes the development and use of 2D colormaps cumbersome. According to our literature research, 2D colormaps have not been subject of in-depth quality assessment. Therefore, we present a survey of static 2D colormaps as applied for information visualization and related fields. In addition, we map seven devised quality assessment measures for 2D colormaps to seven relevant tasks for multivariate data analysis. Finally, we present the quality assessment results of the 2D colormaps with respect to the seven analysis tasks, and contribute guidelines about which colormaps to select or create for each analysis task.

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Bernard, Jürgen; Sessler, David; Bannach, Andreas; May, Thorsten; Kohlhammer, Jörn

A Visual Active Learning System for the Assessment of Patient Well-Being in Prostate Cancer Research

2015

Gschwandtner, Theresia (Conference Chair) et al.: Proceedings of the 2015 Workshop on Visual Analytics in Healthcare. New York: ACM, 2015, Art. 1, 8 p.

Workshop in Visual Analytics in Healthcare (VAHC) <2015, Chicago, IL, USA>

The assessment of patient well-being is highly relevant for the early detection of diseases, for assessing the risks of therapies, or for evaluating therapy outcomes. The knowledge to assess a patient's well-being is actually tacit knowledge and thus, can only be used by the physicians themselves. The rationale of this research approach is to use visual interfaces to capture the mental models of experts and make them available more explicitly. We present a visual active learning system that enables physicians to label the well-being state of patient histories su ering prostate cancer. The labeled instances are iteratively learned in an active learning approach. In addition, the system provides models and visual interfaces for a) estimating the number of patients needed for learning, b) suggesting meaningful learning candidates and c) visual feedback on test candidates. We present the results of two evaluation strategies that prove the validity of the applied model. In a representative real-world use case, we learned the feedback of physicians on a data collection of more than 16.000 prostate cancer histories.

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Bernard, Jürgen; Sessler, David; May, Thorsten; Schlomm, Thorsten; Pehrke, Dirk; Kohlhammer, Jörn

A Visual-Interactive System for Prostate Cancer Cohort Analysis

2015

IEEE Computer Graphics and Applications, Vol.35 (2015), 3, pp. 44-55

Data-centered research is becoming increasingly important in prostate cancer research, where a long-term goal is a sound prognosis prior to surgery. The proposed visual-interactive system, developed in close collaboration with medical researchers, helps physicians efficiently and effectively visualize single and multiple patient histories at a glance.

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Gschwandtner, Theresia; Schumann, Heidrun; Bernard, Jürgen; May, Thorsten; Bögl, Markus; Miksch, Silvia; Kohlhammer, Jörn; Röhlig, Martin; Alsallakh, Bilal

Enhancing Time Series Segmentation and Labeling Through the Knowledge Generation Model: Poster presented at the Eurographics Conference on Visualization (EuroVis)

2015

2015

Eurographics Conference on Visualization (EuroVis) <17, 2015, Cagliari, Sardinia, Italy>

Segmentation and labeling of different activities in multivariate time series data is an important task in many domains. There is a multitude of automatic segmentation and labeling methods available, which are designed to handle different situations. These methods can be used with multiple parametrizations, which leads to an overwhelming amount of options to choose from. To this end, we present a conceptual design of a Visual Analytics framework (1) to select appropriate segmentation and labeling methods with appropriate parametrizations, (2) to analyze the (multiple) results, (3) to understand different kinds and origins of uncertainties in these results, and (4) to reason which methods and which parametrizations yield stable results and fine-tune these configurations if necessary.

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Steiger, Martin; Bernard, Jürgen; Thum, Simon; Mittelstädt, Sebastian; Hutter, Marco; Keim, Daniel A.; Kohlhammer, Jörn

Explorative Analysis of 2D Color Maps

2015

Gavrilova, Marina L. (Ed.) et al.: WSCG 2015. Full Papers Proceedings : 23rd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision. [cited 16 October 2015] Available from http://wscg.zcu.cz/DL/wscg DL.htm: University of West Bohemia, 2015, pp. 151-160

International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG) <23, 2015, Plzen, Czech Republic>

Color is one of the most important visual variables in information visualization. In many cases, two-dimensional information can be color-coded based on a 2D color map. A variety of color maps as well as a number of quality criteria for the use of color have been presented. The choice of the best color map depends on the analytical task users intend to perform and the design space in choosing an appropriate 2D color map is large. In this paper, we present the ColorMap-Explorer, a visual-interactive system that helps users in selecting the most appropriate 2D color map for their particular use case. ColorMap-Explorer also provides a library of many color map implementations that have been proposed in the scientific literature. To analyze their usefulness for different tasks, ColorMap-Explorer provides use case scenarios to allow users to obtain qualitative feedback. In addition, quantitative metrics are provided on a global (i.e. per color map) and local (i.e. per point) scale. ColorMap-Explorer enables users to explore the strengths and weaknesses of existing as well as user-provided color maps to find the best fit for their task. Any color map can be exported to be reused in other visualization tools. The code is published as open source software, so that the visualization community can use both the color map library and the ColorMap-Explorer tool. This also allows users to contribute new implementations.

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Bernard, Jürgen; Fellner, Dieter W. (Betreuer); Schreck, Tobias (Betreuer)

Exploratory Search in Time-Oriented Primary Data

2015

Darmstadt, TU, Diss., 2015

In a variety of research fields, primary data that describes scientific phenomena in an original condition is obtained. Time-oriented primary data, in particular, is an indispensable data type, derived from complex measurements depending on time. Today, time-oriented primary data is collected at rates that exceed the domain experts' abilities to seek valuable information undiscovered in the data. It is widely accepted that the magnitudes of uninvestigated data will disclose tremendous knowledge in data-driven research, provided that domain experts are able to gain insight into the data. Domain experts involved in data-driven research urgently require analytical capabilities. In scientific practice, predominant activities are the generation and validation of hypotheses. In analytical terms, these activities are often expressed in confirmatory and exploratory data analysis. Ideally, analytical support would combine the strengths of both types of activities. Exploratory Search (ES) ES is a concept that seamlessly includes information-seeking behaviors ranging from search to exploration. ES supports domain experts in both gaining an understanding of huge and potentially unknown data collections and the drill-down to relevant subsets, e.g., to validate hypotheses. As such, ES combines predominant tasks of domain experts applied to data-driven research. For the design of useful and usable ES systems (ESS), data scientists have to incorporate different sources of knowledge and technology. Of particular importance is the state-of-the-art in interactive data visualization and data analysis. Research in these factors is at heart of Information Visualization (IV) and Visual Analytics (VA). Approaches in IV and VA provide meaningful visualization and interaction designs, allowing domain experts to perform the information-seeking process in an effective and efficient way. Today, best-practice ESS almost exclusively exist for textual data content, e.g., put into practice in digital libraries to facilitate the reuse of digital documents. For time-oriented primary data, ES mainly remains at a theoretical state. Motivation and Problem Statement This thesis is motivated by two main assumptions. First, we expect that ES will have a tremendous impact on data-driven research for many research fields. In this thesis, we focus on time-oriented primary data, as a complex and important data type for data-driven research. Second, we assume that research conducted to IV and VA will particularly facilitate ES. For time-oriented primary data, however, novel concepts and techniques are required that enhance the design and the application of ESS. In particular, we observe a lack of methodological research in ESS for time-oriented primary data. In addition, the size, the complexity, and the quality of time-oriented primary data hampers the content-based access, as well as the design of visual interfaces for gaining an overview of the data content. Furthermore, the question arises how ESS can incorporate techniques for seeking relations between data content and metadata to foster data-driven research. Overarching challenges for data scientists are to create usable and useful designs, urgently requiring the involvement of the targeted user group and support techniques for choosing meaningful algorithmic models and model parameters. Throughout this thesis, we will resolve these challenges from conceptual, technical, and systemic perspectives. In turn, domain experts can benefit from novel ESS as a powerful analytical support to conduct data-driven research. Contribution In essence, our contributions cover the entire time series analysis process starting from accessing raw time-oriented primary data, processing and transforming time series data, to visual-interactive analysis of time series. We present visual search interfaces providing content-based access to time-oriented primary data. In a series of novel exploration-support techniques, we facilitate both gaining an overview of large and complex time-oriented primary data collections and seeking relations between data content and metadata. Throughout this thesis, we introduce VA as a means of designing effective and efficient visual-interactive systems. Our VA techniques empower data scientists to choose appropriate models and model parameters, as well as to involve users in the design. With both principles, we support the design of usable and useful interfaces which can be included into ESS. In this way, our contributions bridge the gap between search systems requiring exploration support and exploratory data analysis systems requiring visual querying capability. In the ESS presented in two case studies, we prove that our techniques and systems support data-driven research in an efficient and effective way.

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Wilhelm, Nils; Vögele, Anna; Zsoldos, Rebeka; Licka, Theresia; Krüger, Björn; Bernard, Jürgen

FuryExplorer: Visual-Interactive Exploration of Horse Motion Capture Data

2015

Kao, David L. (Ed.) et al.: Visualization and Data Analysis 2015. SPIE Press, 2015. (Proceedings of SPIE 9397), pp. 93970F-1 - 93970F-15

IS&T/SPIE Conference on Visualization and Data Analysis (VDA) <15, 2015, San Francisco, CA, USA>

The analysis of equine motion has a long tradition in the past of mankind. Equine biomechanics aims at detecting characteristics of horses indicative of good performance. Especially, veterinary medicine gait analysis plays an important role in diagnostics and in the emerging research of long-term effects of athletic exercises. More recently, the incorporation of motion capture technology contributed to an easier and faster analysis, with a trend from mere observation of horses towards the analysis of multivariate time-oriented data. However, due to the novelty of this topic being raised within an interdisciplinary context, there is yet a lack of visual-interactive interfaces to facilitate time series data analysis and information discourse for the veterinary and biomechanics communities. In this design study, we bring visual analytics technology into the respective domains, which, to our best knowledge, was never approached before. Based on requirements developed in the domain characterization phase, we present a visual-interactive system for the exploration of horse motion data. The system provides multiple views which enable domain experts to explore frequent poses and motions, but also to drill down to interesting subsets, possibly containing unexpected patterns. We show the applicability of the system in two exploratory use cases, one on the comparison of different gait motions, and one on the analysis of lameness recovery. Finally, we present the results of a summative user study conducted in the environment of the domain experts. The overall outcome was a significant improvement in effectiveness and efficiency in the analytical workflow of the domain experts.

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Gschwandtner, Theresia; Perer, Adam; Bernard, Jürgen

Proceedings of the 2015 Workshop on Visual Analytics in Healthcare

2015

New York : ACM, 2015

Workshop in Visual Analytics in Healthcare (VAHC) <2015, Chicago, IL, USA>

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Bernard, Jürgen; Daberkow, Debora; Fellner, Dieter W.; Fischer, Katrin; Koepler, Oliver; Kohlhammer, Jörn; Runnwerth, Mila; Ruppert, Tobias; Schreck, Tobias; Sens, Irina

VisInfo: A Digital Library System for Time Series Research Data Based on Exploratory Search - a User-centered Design Approach

2015

International Journal on Digital Libraries, Vol.16 (2015), 1, pp. 37-59

To this day, data-driven science is a widely accepted concept in the digital library (DL) context (Hey et al. in The fourth paradigm: data-intensive scientific discovery. Microsoft Research, 2009). In the same way, domain knowledge from information visualization, visual analytics, and exploratory search has found its way into the DL workflow. This trend is expected to continue, considering future DLchallenges such as content-based access to newdocument types, visual search, and exploration for information landscapes, or big data in general. To cope with these challenges, DL actors need to collaborate with external specialists from different domains to complement each other and succeed in given tasks such as making research data publicly available. Through these interdisciplinary approaches, the DL ecosystem may contribute to applications focused on data-driven science and digital scholarship. In this work, we present Vis- Info (2014) , a web-based digital library system (DLS) with the goal to provide visual access to time series research data. Based on an exploratory search (ES) concept (White and Roth in Synth Lect Inf Concepts Retr Serv 1(1):1-98, 2009), VisInfo at first provides a content-based overview visualization of large amounts of time series research data. Further, the system enables the user to define visual queries by example or by sketch. Finally, VisInfo presents visual-interactive capability for the exploration of search results. The development process of VisInfo was based on the user-centered design principle. Experts from computer science, a scientific digital library, usability engineering, and scientists from the earth, and environmental sciences were involved in an interdisciplinary approach. We report on comprehensive user studies in the requirement analysis phase based on paper prototyping, user interviews, screen casts, and user questionnaires. Heuristic evaluations and two usability testing rounds were applied during the system implementation and the deployment phase and certify measurable improvements for our DLS. Based on the lessons learned in VisInfo, we suggest a generalized project workflow that may be applied in related, prospective approaches.

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Steiger, Martin; Bernard, Jürgen; Schader, Philipp; Kohlhammer, Jörn

Visual Analysis of Relations in Attributed Time-Series Data

2015

Bertini, Enrico (Ed.) et al.: EuroVA 2015 : EuroVis Workshop on Visual Analytics. Goslar: Eurographics Association, 2015, pp. 61-65

International EuroVis Workshop on Visual Analytics (EuroVA) <6, 2015, Cagliari, Sardinia, Italy>

In this paper, we present visual-interactive techniques for revealing relations between two co-existing multivariate feature spaces. Such data is generated, for example, by sensor networks characterized by a set of (categorical) attributes which continuously measure physical quantities over time. A challenging analysis task is the seeking for interesting relations between the time-oriented data and the sensor attributes. Our approach uses visualinteractive analysis to enable analysts to identify correlations between similar time series and similar attributes of the data. It is based on a combination of machine-based encoding of this information in position and color and the human ability to recognize cohesive structures and patterns. In our figures, we illustrate how analysts can identify similarities and anomalies between time series and categorical attributes of metering devices and sensors.

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Koldijk, Saskia; Bernard, Jürgen; Ruppert, Tobias; Kohlhammer, Jörn; Neerincx, Mark A.; Kraaij, Wessel

Visual Analytics of Work Behavior Data - Insights on Individual Differences

2015

Kozlíková, Barbora (Ed.) et al.: Eurographics Conference on Visualization (EuroVis) - Short Papers. Goslar: Eurographics Association, 2017, pp. 79-83

Eurographics Conference on Visualization (EuroVis) <17, 2015, Cagliari, Sardinia, Italy>

Stress in working environments is a recent concern. We see potential in collecting sensor data to detect patterns in work behavior with potential danger to well-being. In this paper, we describe how we applied visual analytics to a work behavior dataset, containing information on facial expressions, postures, computer interactions, physiology and subjective experience. The challenge is to interpret this multi-modal low level sensor data. In this work, we alternate between automatic analysis procedures and data visualization. Our aim is twofold: 1) to research the relations of various sensor features with (stress related) mental states, and 2) to develop suitable visualization methods for insight into a large amount of behavioral data. Our most important insight is that people differ a lot in their (stress related) work behavior, which has to be taken into account in the analyses and visualizations.

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Ruppert, Tobias; Bernard, Jürgen; Lücke-Tieke, Hendrik; May, Thorsten; Kohlhammer, Jörn

Visual-Interactive Text Analysis to Support Political Decision Making - From Sentiments to Arguments to Policies

2015

Bertini, Enrico (Ed.) et al.: EuroVA 2015 : EuroVis Workshop on Visual Analytics. Goslar: Eurographics Association, 2015, pp. 37-41

International EuroVis Workshop on Visual Analytics (EuroVA) <6, 2015, Cagliari, Sardinia, Italy>

Political decision making involves the evaluation of alternative solutions (so called policy models) to a given societal problem and the selection of the most promising one. Large amounts of textual information to be considered in decision making processes can be found on the web. This includes general information about policy models, individual arguments in favor or against these policies, and public opinions. Monitoring large text collections and extracting the relevant information is time consuming. In this approach we present a visual analytics system that supports users in assessing the results of automatic text analysis methods. The methods extract text segments from large document collections and associate them with predefined policy domains, policy models, and policy arguments. Moreover, sentiment analysis is applied on the text segments. Visualization techniques provide non-IT experts an intuitive access to the results. With the system, users can monitor public debates on the web. In addition, we analyze concepts that enable the user to give visual-interactive feedback on the text analysis results. This direct user feedback can help to improve the accuracy of individual text analysis modules and the credibility of the overall text analysis process. The system was tested with real users from the political decision making domain.

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Steiger, Martin; Bernard, Jürgen; May, Thorsten; Kohlhammer, Jörn

A Survey of Direction-Preserving Layout Strategies

2014

Gutierrez, Diego (Ed.): Proceedings of the 30th Spring Conference on Computer Graphics : SCCG 2014. New York: ACM, 2014, pp. 21-28

Spring Conference on Computer Graphics (SCCG) <30, 2014, Smolenice, Slovakia>

In this paper we analyze different layout algorithms that preserve relative directions in geo-referenced networks. This is an important criterion for many sensor networks such as the electric grid and other supply networks, because it enables the user to match the geographic setting with the drawing on the screen. Even today, the layouts of these networks are often created manually. This is due to the requirement that these layouts must respect geographic references but should still be easy to read and understand. The range of available automatic algorithms spans from general graph layouts over schematic maps to semi-realistic drawings. At first sight, schematics seem to be a promising compromise between geographic correctness and readability. The former property exploits the mental map of the user while the latter makes it easier for the user to learn about the network structure. We investigate different algorithms for such maps together with different visualization techniques. In particular, the group of octi-linear layouts is prominent in handcrafted subway maps. These algorithms have been used extensively to generate drawings for subway maps. Also known as Metro Map layouts, only horizontal, vertical and diagonal directions are allowed. This increases flexibility and makes the resulting layout look similar to the well-known subway maps of large cities. The key difference to general graph layout algorithms is that geographic relations are respected in terms of relative directions. However, it is not clear, whether this metaphor can be transferred from metro maps to other domains. We discuss applicability of these different approaches for geo-based networks in general with the electric grid as a use-case scenario

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Ruppert, Tobias; Bernard, Jürgen; May, Thorsten; Kohlhammer, Jörn

Combining Computational Models and Interactive Visualization to Support Rational Decision Making

2014

Bebis, George (Ed.) et al.: Advances in Visual Computing. 10th International Symposium, ISVC 2014 : Proceedings, Part I. Berlin, Heidelberg, New York: Springer, 2014. (Lecture Notes in Computer Science (LNCS) 8887), pp. 345-356

International Symposium on Visual Computing (ISVC) <10, 2014, Las Vegas, NV, USA>

Decision making is a complex process consisting of several consecutive steps. Before converting a decision into effective action the problem to be tackled needs to be analyzed, alternative solutions need to be developed, and the best solution needs to be picked. In many cases computational models support decision makers in this process. Therefore, providing an intuitive access to these model-driven techniques is crucial. In this approach, we introduce a decision support system that provides visual-interactive access to three computational models - a simulation model, an optimization model, and an opinion mining model - covering different aspects of decision making. For each model our decision support system realizes the visual access to the model, an in-depth analysis of the generated solutions, and the comparison of alternative solutions. Finally, we evaluate the usefulness and the usability of our system in a use case in the field of public policy making.

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Mittelstädt, Sebastian; Bernard, Jürgen; Schreck, Tobias; Steiger, Martin; Kohlhammer, Jörn; Keim, Daniel A.

Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis

2014

Elmqvist, N. (Ed.) et al.: EuroVis - Short Papers 2014. Goslar: Eurographics Association, 2014, pp. 91-95

Eurographics Conference on Visualization (EuroVis) <16, 2014, Swansea, Wales, UK>

Colors is one of the most effective visual variables since it can be combined with other mappings and encode information without using any additional space on the display. An important example where expressing additional visual dimensions is direly needed is the analysis of high-dimensional data. The property of perceptual linearity is desirable in this application, because the user intuitively perceives clusters and relations among multi-dimensional data points. Many approaches use two-dimensional colormaps in their analysis, which are typically created by interpolating in RGB, HSV or CIELAB color spaces. These approaches share the problem that the resulting colors are either saturated and discriminative but not perceptual linear or vice versa. A solution that combines both advantages has been previously introduced by Kaski et al.; yet, this method is to date underutilized in Information Visualization according to our literature analysis. The method maps high-dimensional data points into the CIELAB color space by maintaining the relative perceived distances of data points and color discrimination. In this paper, we generalize and extend the method of Kaski et al. to provide perceptual uniform color mapping for visual analysis of high-dimensional data. Further, we evaluate the method and provide guidelines for different analysis tasks.

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Ruppert, Tobias; Bernard, Jürgen; Lücke-Tieke, Hendrik; Kohlhammer, Jörn

Towards a Tighter Coupling of Visualization and Public Policy Making

2014

Chen, Min (Ed.) et al.: IEEE Conference on Visual Analytics Science and Technology. Proceedings : VAST 2014. Los Alamitos, Calif.: IEEE Computer Society, 2014, pp. 271-272

IEEE Symposium on Visual Analytics Science and Technology (VAST) <9, 2014, Paris, France>

The purpose of this ongoing work is to motivate public policy making as an application area for information visualization and visual analytics. Through our expertise gathered in several policy making related projects, we identified parallels between the benefits of visualization and the needs of evidence-based public policy making. In the following, we will share our previous work consisting of the conceptual introduction of information visualization and visual analytics into the application field of public policy making. Moreover, we will show two real-world cases applying this concept. Finally, we will share identified challenges to be addressed by the information visualization and visual analytics domains in the future.

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Bernard, Jürgen; Hutter, Marco; Sessler, David; Schreck, Tobias; Behrisch, Michael; Kohlhammer, Jörn

Towards a User-Defined Visual-Interactive Definition of Similarity Functions for Mixed Data

2014

Chen, Min (Ed.) et al.: IEEE Conference on Visual Analytics Science and Technology. Proceedings : VAST 2014. Los Alamitos, Calif.: IEEE Computer Society, 2014, pp. 227-228

IEEE Symposium on Visual Analytics Science and Technology (VAST) <9, 2014, Paris, France>

The creation of similarity functions based on visual-interactive user feedback is a promising means to capture the mental similarity notion in the heads of domain experts. In particular, concepts exist where users arrange multivariate data objects on a 2D data landscape in order to learn new similarity functions. While systems that incorporate numerical data attributes have been presented in the past, the remaining overall goal may be to develop systems also for mixed data sets. In this work, we present a feedback model for categorical data which can be used alongside of numerical feedback models in future.

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Bernard, Jürgen; Sessler, David; Ruppert, Tobias; Davey, James; Kuijper, Arjan; Kohlhammer, Jörn

User-Based Visual-Interactive Similarity Definition for Mixed Data Objects - Concept and First Implementation

2014

Skala, Vaclav (Ed.): WSCG 2014. Communication Papers Proceedings : 22nd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision. Plzen: University of West Bohemia, 2014, pp. 329-338

International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG) <22, 2014, Plzen, Czech Republic>

The definition of similarity between data objects plays a key role in many analytical systems. The process of similarity definition comprises several challenges as three main problems occur: different stakeholders, mixed data, and changing requirements. Firstly, in many applications the developers of the analytical system (data scientists) model the similarity, while the users (domain experts) have distinct (mental) similarity notions. Secondly, the definition of similarity for mixed data types is challenging. Thirdly, many systems use static similarity models that cannot adapt to changing data or user needs. We present a concept for the development of systems that support the visual-interactive similarity definition for mixed data objects emphasizing 15 crucial steps. For each step different design considerations and implementation variants are presented, revealing a large design space. Moreover, we present a first implementation of our concept, enabling domain experts to express mental similarity notions through a visual-interactive system. The provided implementation tackles the different-stakeholders problem, the mixed data problem, and the changing requirements problem. The implementation is not limited to a specific mixed data set. However, we show the applicability of our implementation in a case study where a functional similarity model is trained for countries as objects.

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Sessler, David; Kuijper, Arjan (Betreuer); Bernard, Jürgen (Betreuer)

User-centered Interactive Similarity Definition for Complex Data Objects

2014

Darmstadt, TU, Bachelor Thesis, 2014

The definition of similarity between data objects plays a key role for the applicability of many analytical systems. Similarity measures are used for prominent data analysis tasks like nearest neighbor search, clustering, or pattern recognition. These tasks are applied in many scientific domains like Information Retrieval, Data Mining, Machine Learning, Information Visualization and Visual Analytics. The data used for the calculation of similarity can either be of uniform attribute type (like numerical, ordinal, categorical or binary) or consist of combinations thereof (mixed data). The process of similarity definition comprises several challenges which I aim to tackle in this work. To start with, in many applications the developers (data experts) of the analytical system are not necessarily the users (domain experts) of the system. A problem arises, because data experts implement the functional similarity specification for domain experts. The functional similarity specification, however, should reflect the similarity notion in the minds of domain experts. Therefore the domain experts should be involved in the similarity generation process. The second challenge refers to the similarity definition for mixed data. A variety of similarity definitions for numerical, categorical or binary data exist. However, the similarity definition based on mixed data is cumbersome because of the complexity of the data. Finally, there are two possibilities when the similarity can be defined, namely at compile time or at run time. Today, many analytical systems define the similarity at compile time. However, the similarity notion of domain experts or the data set may vary over time. This would require a new specification of the functional similarity and a new compilation of the system. The definition of similarity at run time would solve this problem. I present a visual-interactive system that enables domain experts to define a similarity measure that reflects their similarity notion. The system is applicable for mixed data sets. Domain experts can align objects in a visual interface to generate feedback. Dynamic recalculation of the functional similarity specification allows to match the similarity notion of domain expert at run time. This way the functional similarity specification can be adjusted at any time. Further, I provide a visual-interactive mode which enables the data expert to explore the similarity definition process of the domain expert. In addition, I evaluate the system to assess the quality of the similarity concept as well as the feedback generation process. The results of the evaluation illustrate both: the validity of my solution as well as extension possibilities depending on the complexity of the given user feedback. In two case studies I show the applicability of the system. Both use cases show that the 'mental' similarity notion of users can be captured by the similarity concept. The results of the evaluation and the observations made in the case studies can be applied to improve the system or be used as a baseline for future approaches for user-centered interactive similarity definition for complex data objects.

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Ruppert, Tobias; Bernard, Jürgen; Ulmer, Alex; Lücke-Tieke, Hendrik; Kohlhammer, Jörn

Visual Access to an Agent-based Simulation Model to Support Political Decision Making

2014

Lindstaedt, Stefanie (Ed.) et al.: i-KNOW 2014 : Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business. New York: ACM, 2014. (ACM International Conference Proceedings Series 889), Article 16, 8 p.

International Conference on Knowledge Technologies and Data-driven Business (I-KNOW) <14, 2014, Graz, Austria>

Decision making in the field of policy making is a complex task. On the one hand conflicting objectives influence the availability of alternative solutions for a given problem. On the other hand economic, social, and environmental impacts of the chosen solution have to be considered. In the political context, these solutions are called policy options. To tackle societal problems a thorough analysis of policy options needs to be executed before a policy can be put into practice. Computational simulation is a method considered for measuring the impacts of policy options. However, due to their complexity, the underlying models and their output may be difficult to access by decision makers. In this work, we present a visual-interactive interface for an agent-based simulation model that enables decision makers to evaluate the impacts of alternative policy options in the field of regional energy planning. The decision maker can specify different subsidy strategies for supporting public photovoltaic installations as input and evaluate their impact on the actual adoption via the simulation output. We show the usability and usefulness of the visual interface in a real-world example evolved from the European research project ePolicy.

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Steiger, Martin; Bernard, Jürgen; Mittelstädt, Sebastian; Lücke-Tieke, Hendrik; Keim, Daniel A.; May, Thorsten; Kohlhammer, Jörn

Visual Analysis of Time-Series Similarities for Anomaly Detection in Sensor Networks

2014

Computer Graphics Forum, Vol.33 (2014), 3, pp. 401-410

Eurographics Conference on Visualization (EuroVis) <16, 2014, Swansea, Wales, UK>

We present a system to analyze time-series data in sensor networks. Our approach supports exploratory tasks for the comparison of univariate, geo-referenced sensor data, in particular for anomaly detection. We split the recordings into fixed-length patterns and show them in order to compare them over time and space using two linked views. Apart from geo-based comparison across sensors we also support different temporal patterns to discover seasonal effects, anomalies and periodicities. The methods we use are best practices in the information visualization domain. They cover the daily, the weekly and seasonal and patterns of the data. Daily patterns can be analyzed in a clustering-based view, weekly patterns in a calendar-based view and seasonal patterns in a projection-based view. The connectivity of the sensors can be analyzed through a dedicated topological network view. We assist the domain expert with interaction techniques to make the results understandable. As a result, the user can identify and analyze erroneous and suspicious measurements in the network. A case study with a domain expert verified the usefulness of our approach.

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Bernard, Jürgen; Steiger, Martin; Widmer, Sven; Lücke-Tieke, Hendrik; May, Thorsten; Kohlhammer, Jörn

Visual-interactive Exploration of Interesting Multivariate Relations in Mixed Research Data Sets

2014

Computer Graphics Forum, Vol.33 (2014), 3, pp. 291-300

Eurographics Conference on Visualization (EuroVis) <16, 2014, Swansea, Wales, UK>

The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.

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Nazemi, Kawa; Retz, Reimond; Bernard, Jürgen; Kohlhammer, Jörn; Fellner, Dieter W.

Adaptive Semantic Visualization for Bibliographic Entries

2013

Bebis, George (Ed.) et al.: Advances in Visual Computing. 9th International Symposium, ISVC 2013 : Proceedings, Part II. Berlin, Heidelberg, New York: Springer, 2013. (Lecture Notes in Computer Science (LNCS) 8034), pp. 13-24

International Symposium on Visual Computing (ISVC) <9, 2013, Rethymnon, Crete, Greece>

Adaptive visualizations aim to reduce the complexity of visual representations and convey information using interactive visualizations. Although the research on adaptive visualizations grew in the last years, the existing approaches do not make use of the variety of adaptable visual variables. Further the existing approaches often premises experts, who has to model the initial visualization design. In addition, current approaches either incorporate user behavior or data types. A combination of both is not proposed to our knowledge. This paper introduces the instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on visual layout and visual presentation in a multiple visualization environment. Based on data type and users' behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonic requirements on both, data types and users' behavior. Our system does not require an initial expert modeling.

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Ruppert, Tobias; Bernard, Jürgen; Kohlhammer, Jörn

Bridging Knowledge Gaps in Policy Analysis with Information Visualization

2013

Wimmer, Maria A. (Ed.) et al.: Electronic Government and Electronic Participation : Joint Proceedings of Ongoing Research of IFIP EGOV and IFIP ePart 2013. Bonn: Köllen, 2013. (GI-Edition - Lecture Notes in Informatics (LNI) P-221), pp. 92-103

International conferences on Electronic GOVernment (EGOV) <2013, Koblenz, Germany>

Today's politicians are confronted with new (digital) ways to tackle complex decision-making problems. In order to make the right decisions profound analysis of the problems and possible solutions has to be performed. Therefore policy analysts need to collaborate with external experts consulted as advisors. Due to different expertises of these stakeholders the whole process may suffer from knowledge gaps. In our approach, we describe a concept to bridge these knowledge gaps by introducing information visualization and visual analytics to the policy analysis domain. Therefore, we refine a standard policy cycle at the stages relevant for the policy analysis. Secondly, we characterize the main stakeholders in the process, and identify knowledge gaps between these roles. Finally, we emphasize the merits of including advanced visualization techniques into the policy analysis process, and describe visualization as a facet bridging the knowledge gaps in a collaborative policy making life-cycle.

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Bernard, Jürgen; Wilhelm, Nils; Krüger, Björn; May, Thorsten; Schreck, Tobias; Kohlhammer, Jörn

MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation

2013

IEEE Transactions on Visualization and Computer Graphics, Vol.19 (2013), 12, pp. 2257-2266

IEEE Symposium on Visual Analytics Science and Technology (VAST) <8, 2013, Atlanta, GA, USA>

We present MotionExplorer, an exploratory search and analysis system for sequences of human motion in large motion capture data collections. This special type of multivariate time series data is relevant in many research fields including medicine, sports and animation. Key tasks in working with motion data include analysis of motion states and transitions, and synthesis of motion vectors by interpolation and combination. In the practice of research and application of human motion data, challenges exist in providing visual summaries and drill-down functionality for handling large motion data collections. We find that this domain can benefit from appropriate visual retrieval and analysis support to handle these tasks in presence of large motion data. To address this need, we developed MotionExplorer together with domain experts as an exploratory search system based on interactive aggregation and visualization of motion states as a basis for data navigation, exploration, and search. Based on an overview-first type visualization, users are able to search for interesting sub-sequences of motion based on a query-by-example metaphor, and explore search results by details on demand. We developed MotionExplorer in close collaboration with the targeted users who are researchers working on human motion synthesis and analysis, including a summative field study. Additionally, we conducted a laboratory design study to substantially improve MotionExplorer towards an intuitive, usable and robust design. MotionExplorer enables the search in human motion capture data with only a few mouse clicks. The researchers unanimously confirm that the system can efficiently support their work.

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Ruppert, Tobias; Bernard, Jürgen; Ulmer, Alex; Kuijper, Arjan; Kohlhammer, Jörn

Visual Access to Optimization Problems in Strategic Environmental Assessment

2013

Bebis, George (Ed.) et al.: Advances in Visual Computing. 9th International Symposium, ISVC 2013 : Proceedings, Part II. Berlin, Heidelberg, New York: Springer, 2013. (Lecture Notes in Computer Science (LNCS) 8034), pp. 361-372

International Symposium on Visual Computing (ISVC) <9, 2013, Rethymnon, Crete, Greece>

The complexity of actual decision making problems especially in the field of policy making is increasing due to conflicting aspects to be considered. Methods from the field of strategic environmental assessment consider environmental, economic, and social impacts caused by political decisions. This makes the analysis of reasonable decisions more complex. Mathematical models like optimization can help to balance conflicting aspects. Although they are not easy to understand, these complex models and the resulting policy options have to be reviewed by the decision makers. In this work we present a visual-interactive interface to an optimization system capable of solving multidimensional decision problems. The interface enables visual access to the complex optimization models, and the analysis of alternative solutions. As a result strategic environmental assessment can be included in the decision making process. An evaluation in the domain of regional energy planning underlines the usability and usefulness of the visual interface.

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Bernard, Jürgen; Ruppert, Tobias; Scherer, Maximilian; Kohlhammer, Jörn; Schreck, Tobias

Content-Based Layouts for Exploratory Metadata Search in Scientific Research Data

2012

Association for Computing Machinery (ACM): JCDL 2012. Proceedings : 12th ACM/IEEE-CS Joint Conference on Digital Libraries. New York: ACM, 2012, pp. 139-148

ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL) <12, 2012, Washington, DC, USA>

Today's digital libraries (DLs) archive vast amounts of information in the form of text, videos, images, data measurements, etc. User access to DL content can rely on similarity between metadata elements, or similarity between the data itself (content-based similarity). We consider the problem of exploratory search in large DLs of time-oriented data. We propose a novel approach for overview-first exploration of data collections based on user-selected metadata properties. In a 2D layout representing entities of the selected property are laid out based on their similarity with respect to the underlying data content. The display is enhanced by compact summarizations of underlying data elements, and forms the basis for exploratory navigation of users in the data space. The approach is proposed as an interface for visual exploration, leading the user to discover interesting relationships between data items relying on content-based similarity between data items and their respective metadata labels. We apply the method on real data sets from the earth observation community, showing its applicability and usefulness.

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Bernard, Jürgen; Ruppert, Tobias; Scherer, Maximilian; Schreck, Tobias; Kohlhammer, Jörn

Guided Discovery of Interesting Relationships Between Time Series Clusters and Metadata Properties

2012

Lindstaedt, Stefanie (Ed.) et al.: i-KNOW '12. Proceedings : 12th International Conferences on Knowledge Management and Knowledge Technologies. New York: ACM, 2012. (ACM International Conference Proceedings Series 642), pp. 22:1-22:8

International Conference on Knowledge Management and Knowledge Technologies (I-KNOW) <12, 2012, Graz, Austria>

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, especially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.

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Bernard, Jürgen; Wilhelm, Nils; Scherer, Maximilian; May, Thorsten; Schreck, Tobias

TimeSeriesPaths: Projection-Based Explorative Analysis of Multivariate Time Series Data

2012

Skala, Vaclav (Ed.): Journal of WSCG Vol. 20 No. 1-3, 2012. Proceedings. Plzen: University of West Bohemia, 2012, pp. 97-106

International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG) <20, 2012, Plzen, Czech Republic>

The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth observation, demonstrating the applicability and usefulness of our approach.

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Schreck, Tobias; Sharalieva, Lyubka; Wanner, Franz; Bernard, Jürgen; Ruppert, Tobias; Landesberger, Tatiana von; Bustos, Benjamin

Visual Exploration of Local Interest Points in Sets of Time Series

2012

Santucci, Giuseppe (Ed.) et al.: IEEE Conference on Visual Analytics Science and Technology 2012. Proceedings : VAST 2012. New York: IEEE Press, 2012, pp. 239-240

IEEE Symposium on Visual Analytics Science and Technology (VAST) <7, 2012, Seattle, WA, USA>

Visual analysis of time series data is an important, yet challenging task with many application examples in fields such as financial or news stream data analysis. Many visual time series analysis approaches consider a global perspective on the time series. Fewer approaches consider visual analysis of local patterns in time series, and often rely on interactive specification of the local area of interest. We present initial results of an approach that is based on automatic detection of local interest points. We follow an overview-first approach to find useful parameters for the interest point detection, and details-on-demand to relate the found patterns. We present initial results and detail possible extensions of the approach.

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Bernard, Jürgen; Ruppert, Tobias; Goroll, Oliver; May, Thorsten; Kohlhammer, Jörn

Visual-Interactive Preprocessing of Time Series Data

2012

Kerren, Andreas (Ed.) et al.: Proceedings of SIGRAD 2012 : Interactive Visual Analysis of Data. Linköping: Linköping University Electronic Press, 2012. (Linköping Electronic Conference Proceedings 81), pp. 39-48

SIGRAD Conference <11, 2012, Växjö, Sweden>

Time series data is an important data type in many different application scenarios. Consequently, there are a great variety of approaches for analyzing time series data. Within these approaches different strategies for cleaning, segmenting, representing, normalizing, comparing, and aggregating time series data can be found. When combining these operations, the time series analysis preprocessing workflow has many degrees of freedom. To define an appropriate preprocessing pipeline, the knowledge of experts coming from the application domain has to be included into the design process. Unfortunately, these experts often cannot estimate the effects of the chosen preprocessing algorithms and their parameterizations on the time series. We introduce a system for the visual-interactive exploitation of the preprocessing parameter space. In contrast to 'black box'-driven approaches designed by computer scientists based on the requirements of domain experts, our system allows these experts to visual-interactively compose time series preprocessing pipelines by themselves. Visual support is provided to choose the right order and parameterization of the preprocessing steps. We demonstrate the usability of our approach with a case study from the digital library domain, in which time-oriented scientific research data has to be preprocessed to realize a visual search and analysis application.

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Bernard, Jürgen; Brase, Jan; Fellner, Dieter W.; Koepler, Oliver; Kohlhammer, Jörn; Ruppert, Tobias; Schreck, Tobias; Sens, Irina

A Visual Digital Library Approach for Time-Oriented Scientific Primary Data

2011

International Journal on Digital Libraries, Vol.11 (2011), 2, pp. 111-123

European Conference on Research and Advanced Technology for Digital Libraries (ECDL) <14, 2010, Glasgow, UK>

Digital Library support for textual and certain types of non-textual documents has significantly advanced over the last years. While Digital Library support implies many aspects along the whole library workflow model, interactive and visual retrieval allowing effective query formulation and result presentation are important functions. Recently, new kinds of non-textual documents which merit Digital Library support, but yet cannot be fully accommodated by existing Digital Library technology, have come into focus. Scientific data, as produced for example, by scientific experimentation, simulation or observation, is such a document type. In this article we report on a concept and first implementation of Digital Library functionality for supporting visual retrieval and exploration in a specific important class of scientific primary data, namely, time-oriented research data. The approach is developed in an interdisciplinary effort by experts from the library, natural sciences, and visual analytics communities. In addition to presenting the concept and to discussing relevant challenges, we present results from a first implementation of our approach as applied on a real-world scientific primary data set. We also report from initial user feedback obtained during discussions with domain experts from the earth observation sciences, indicating the usefulness of our approach.

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Bremm, Sebastian; Landesberger, Tatiana von; Bernard, Jürgen; Schreck, Tobias

Assisted Descriptor Selection Based on Visual Comparative Data Analysis

2011

Computer Graphics Forum, Vol.30 (2011), 3, pp. 891-900

Eurographics / IEEE Symposium on Visualization (EuroVis) <13, 2011, Bergen, Norway>

Exploration and selection of data descriptors representing objects using a set of features are important components in many data analysis tasks. Usually, for a given dataset, an optimal data description does not exist, as the suitable data representation is strongly use case dependent. Many solutions for selecting a suitable data description have been proposed. In most instances, they require data labels and often are black box approaches. Non-expert users have difficulties to comprehend the coherency of input, parameters, and output of these algorithms. Alternative approaches, interactive systems for visual feature selection, overburden the user with an overwhelming set of options and data views. Therefore, it is essential to offer the users guidance in this analytical process. In this paper, we present a novel system for data description selection, which facilitates the user's access to the data analysis process. As finding of suitable data description consists of several steps, we support the user with guidance. Our system combines automatic data analysis with interactive visualizations. By this, the system provides a recommendation for suitable data descriptor selections. It supports the comparison of data descriptors with differing dimensionality for unlabeled data. We propose specialized scores and interactive views for descriptor comparison. The visualization techniques are scatterplot-based and grid-based. For the latter case, we apply Self-Organizing Maps as adaptive grids which are well suited for large multi-dimensional data sets. As an example, we demonstrate the usability of our system on a real-world biochemical application.

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Bernard, Jürgen; Landesberger, Tatiana von; Bremm, Sebastian; Schreck, Tobias

Multi-Scale Visual Quality Assessment for Cluster Analysis with Self-Organizing Maps

2011

Wong, Pak Chung (Ed.) et al.: Visualization and Data Analysis 2011. Bellingham: SPIE Press, 2011. (Proceedings of SPIE 7868), pp. 78680N-1 - 78680N-12

SPIE Conference on Visualization and Data Analysis (VDA) <11, 2011, San Francisco, CA, USA>

Cluster analysis is an important data mining technique for analyzing large amounts of data, reducing many objects to a limited number of clusters. Cluster visualization techniques aim at supporting the user in better understanding the characteristics and relationships among the found clusters. While promising approaches to visual cluster analysis already exist, these usually fall short of incorporating the quality of the obtained clustering results. However, due to the nature of the clustering process, quality plays an important aspect, as for most practical data sets, typically many different clusterings are possible. Being aware of clustering quality is important to judge the expressiveness of a given cluster visualization, or to adjust the clustering process with refined parameters, among others. In this work, we present an encompassing suite of visual tools for quality assessment of an important visual cluster algorithm, namely, the Self-Organizing Map (SOM) technique. We define, measure, and visualize the notion of SOM cluster quality along a hierarchy of cluster abstractions. The quality abstractions range from simple scalar-valued quality scores up to the structural comparison of a given SOM clustering with output of additional supportive clustering methods. The suite of methods allows the user to assess the SOM quality on the appropriate abstraction level, and arrive at improved clustering results. We implement our tools in an integrated system, apply it on experimental data sets, and show its applicability.

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Bernard, Jürgen; Brase, Jan; Fellner, Dieter W.; Koepler, Oliver; Kohlhammer, Jörn; Ruppert, Tobias; Schreck, Tobias; Sens, Irina

A Visual Digital Library Approach for Time-Oriented Scientific Primary Data

2010

Lalmas, Mounia (Ed.) et al.: Research and Advanced Technology for Digital Libraries : 14th European Conference ECDL. Proceedings. Berlin, Heidelberg, New York: Springer, 2010. (Lecture Notes in Computer Science (LNCS) 6273), pp. 352-363

European Conference on Research and Advanced Technology for Digital Libraries (ECDL) <14, 2010, Glasgow, UK>

Digital Library support for textual and certain types of non-textual documents has significantly advanced over the last years. While Digital Library support implies many aspects along the whole library workflow model, interactive and visual retrieval allowing effective query formulation and result presentation are important functions. Recently, new kinds of non-textual documents which merit Digital Library support, but yet cannot be accommodated by existing Digital Library technology, have come into focus. Scientific primary data, as produced for example, by scientific experimentation, earth observation, or simulation, is such a data type. We report on a concept and first implementation of Digital Library functionality, supporting visual retrieval and exploration in a specific important class of scientific primary data, namely, time-oriented data. The approach is developed in an interdisciplinary effort by experts from the library, natural sciences, and visual analytics communities. In addition to presenting the concept and discussing relevant challenges, we present results from a first implementation of our approach as applied on a real-world scientific primary data set.

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Bernard, Jürgen; Landesberger, Tatiana von; Bremm, Sebastian; Schreck, Tobias

Cluster Correspondence Views for Enhanced Analysis of SOM Displays

2010

MacEachren, Alan M. (Ed.) et al.: IEEE Conference on Visual Analytics Science and Technology 2010. Proceedings : VAST 2010. New York: IEEE Press, 2010, pp. 217-218

IEEE Symposium on Visual Analytics Science and Technology (VAST) <5, 2010, Salt Lake City, UT, USA>

The Self-Organizing Map (SOM) algorithm is a popular and widely used cluster algorithm. Its constraint to organize clusters on a grid structure makes it very amenable to visualization. On the other hand, the grid constraint may lead to reduced cluster accuracy and reliability, compared to other clustering methods not implementing this restriction. We propose a visual cluster analysis system that allows to validate the output of the SOM algorithm by comparison with alternative clustering methods. Specifically, visual mappings overlaying alternative clustering results onto the SOM are proposed. We apply our system on an example data set, and outline main analytical use cases.

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Landesberger, Tatiana von; Bremm, Sebastian; Bernard, Jürgen; Schreck, Tobias

Smart Query Definition for Content-Based Search in Large Sets of Graphs

2010

Kohlhammer, Jörn (Ed.) et al.: EuroVAST 2010 : International Symposium on Visual Analytics Science and Technology. Goslar: Eurographics Association, 2010, pp. 7-12

International Symposium on Visual Analytics Science and Technology (EuroVAST) <1, 2010, Bordeaux, France>

Graphs are used in various application areas such as chemical, social or shareholder network analysis. Finding relevant graphs in large graph databases is thereby an important problem. Such search starts with the definition of the query object. Defining the query graph quickly and effectively so that it matches meaningful data in the database is difficult. In this paper, we introduce a system, which guides the user through the process of query graph building. We propose three approaches for graph definition. First, query by example selection starting from an overview of the graph types in the database, second query by sketch combining graph building blocks (i.e., topologic subgraphs) with free graph drawing, and third a combination of both approaches. In all three query definition ways, we support the user with intelligent, data dependent recommendations. It covers the whole spectrum of building parameters such as representative examples, frequent building blocks, or common graph size.

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Bernard, Jürgen; Schreck, Tobias (Betreuer); Landesberger, Tatiana von (Betreuer)

Methoden zur Qualitätsbewertung von Selforganizing Maps zur Unterstützung des visuellen Analyseprozesses

2009

Darmstadt, TU, Diplomarbeit, 2009

Der Self-organizing Maps Algorithmus (SOM) erfreut sich in der visuellen Clusteranalyse großer Beliebtheit. Hauptgründe hierfür sind seine topologieerhaltende Eigenschaft bei der Partitionierung von hochdimensionalen Datensätzen und die leichte Visualisierbarkeit von Ergebnissen aufgrund der SOM-Gitterstruktur. Seit ihrer Einführung, wurden SOMs in einer Vielzahl unterschiedlicher Data Mining Anwendungen verwendet. Einhergehend mit dem technischen Fortschritt von Computersystemen, haben sich die Visualisierungs- und Interaktionstechniken in der visuellen Clusteranalyse kontinuierlich verbessert. Maßgeblichen Anteil an dieser Entwicklung haben die Fachgebiete der Informationsvisualisierung und die aufstrebende Disziplin der Visual Analytics. In jüngster Zeit wurden zukunftsweisende, visuell-interaktive Repräsentierungen des SOM-Algorithmus zur Informationsbeschaffung aus komplexen Datensätzen vorgestellt. Ein aktueller Trend der Visual Analytics ist die visuell-interaktive Erschließung des gesamten Clusteranalyseprozesses, beginnend mit der Datenvorverarbeitung, bis hin zur Evaluierung von Clusteringergebnissen. Dies schließt auch die schrittweise Verbesserung der erreichten Clusteringqualität mit ein (Iterative Refinement). Die durch gesteigerte Einflussmöglichkeiten des Menschen vollzogene Distanzierung von vollautomatischen Clusteranalysetools, birgt enormes Potential bei der Exploration von Daten. In gleicher Weise gilt es jedoch, potentielle Schwierigkeiten neuartiger Clusteranalysetools zu berücksichtigen. Besondere Aufmerksamkeit bedarf es in diesem Zusammenhang der Bewertung und Visualisierung der erreichten SOM-Clusteringqualität, die durch erweiterte Möglichkeiten der Einflussnahme maßgeblich vom Anwender abhängig ist. Die Notwendigkeit von Werkzeugen zur visuellen Qualitätsbewertung erstreckt sich hierbei vom Prozessschritt des Iterative Refinements bis weit in die Postprocessingphase des Clusterings hinein. Das Ziel dieser Diplomarbeit besteht in der Entwicklung von Lösungsstrategien um den SOMClusteranalyseprozess mit visuellen Qualitätsbewertungsmethoden zu unterstützen. Umgesetzt werden die Ergebnisse im FinExplorer Framework, einer SOM-basierten Applikation zur visuellen Analyse von Finanzdaten, die um entsprechende Methoden zur visuellen Qualitätsbewertung erweitert wird. Neben der Integration bewährter Darstellungsformen, liegt der Schwerpunk dieser Arbeit in der Konzeption, der Umsetzung und der Evaluation neu entwickelter Visualisierungsformen. Der Umfang der Arbeit erstreckt sich dabei über die Erarbeitung von trainingsbegleitenden Qualitätsvisualisierungen, die Erschließung und Visualisierung von geeigneten statistischen Qualitätsindices, die verbesserte Einsicht auf die SOM-Struktur mit ihrem zugrundeliegenden Datensatz und schließlich auf die komparative Darstellung der SOM mit Ergebnissen aus anderen Clusteringalgorithmen.

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Schreck, Tobias; Bernard, Jürgen; Landesberger, Tatiana von; Kohlhammer, Jörn

Visual Cluster Analysis of Trajectory Data With Interactive Kohonen Maps

2009

Information Visualization, Vol.8 (2009), 1, pp. 14-29

Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Due to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or Self-Organizing Map, or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations, or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on a trajectory clustering problem, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results.

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Schreck, Tobias; Bernard, Jürgen; Tekusová, Tatiana; Kohlhammer, Jörn

Visual Cluster Analysis of Trajectory Data With Interactive Kohonen Maps

2008

Ebert, David S. (Ed.) et al.: IEEE Symposium on Visual Analytics Science and Technology 2008. Proceedings : VAST 2008. New York: IEEE Press, 2008, pp. 3-10

IEEE Symposium on Visual Analytics Science and Technology (VAST) <3, 2008, Columbus, OH, USA>

Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Due to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or Self-Organizing Map, or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations, or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on a trajectory clustering problem, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results.