Visual Analytics to Support Evidence-Based Decision Making
Darmstadt, TU, Diss., 2018
The aim of this thesis is the design of visual analytics solutions to support evidence-based decision making. Due to the ever-growing complexity of the world, strategical decision making has become an increasingly challenging task. At the business level, decisions are not solely driven by economic factors anymore. Environmental and social aspects are also taken into account in modern business decisions. At the political level, sustainable decision making is additionally influenced by the public opinion, since politicians target the conservation of their power. Decision makers face the challenge of taking all these factors into consideration and, at the same time, of increasing their efficiency to immediately react on abrupt changes in their environment. Due to the digitization era, large amounts of data are digitally stored. The knowledge hidden in these datasets can be used to address the mentioned challenges in decision making. However, handling large datasets, extracting knowledge from them, and incorporating this knowledge into the decision making process poses significant challenges. Additional complexity is added by the varying expertises of stakeholders involved in the decision making process. Strategical decisions today are not solely made by individuals. In contrast, a consortium of advisers, domain experts, analysts, etc. support decision makers in their final choice. The amount of involved stakeholders bears the risk of hampering communication efficiency and effectiveness due to knowledge gaps coming from different expertise levels. Information systems research has reacted to these challenges by promoting research in computational decision support systems. However, recent research shows that most of the challenges remain unsolved. During the last decades, visual analytics has evolved as a research field for extracting knowledge from large datasets. Therefore, combining human perception capabilities and computers' processing power offers great analysis potential, also for decision making. However, despite obvious overlaps between decision making and visual analytics, theoretical foundations for applying visual analytics to decision making have been missing. In this thesis, we promote the augmentation of decision support systems with visual analytics. Our concept comprises a methodology for the design of visual analytics systems that target decision making support. Therefore, we first introduce a general decision making domain characterization, comprising the analysis of potential users, relevant data categories, and decision making tasks to be supported with visual analytics technologies. Second, we introduce a specialized design process for the development of visual analytics decision support systems. Third, we present two models on how visual analytics facilitates the bridging of knowledge gaps between stakeholders involved in the decision making process: one for decision making at the business level and one for political decision making. To prove the applicability of our concepts, we apply our design methodology in several design studies targeting concrete decision making support scenarios. The presented design studies cover the full range of data, user, and task categories characterized as relevant for decision making. Within these design studies, we first tailor our general decision making domain characterization to the specific domain problem at hand. We show that our concept supports a consistent characterization of user types, data categories and decision making tasks for specific scenarios. Second, each design study follows the design process presented in our concept. And third, the design studies demonstrate how to bridge knowledge gaps between stakeholders. The resulting visual analytics systems allow the incorporation of knowledge extracted from data into the decision making process and support the collaboration of stakeholders with varying levels of expertises.
Visualization of Zoomable 2D Projections on the Web
HCI in Business, Government, and Organizations
International Conference on Human-Computer Interaction in Business, Government and Organization (HCIBGO) <5, 2018, Las Vegas, NV, USA>
Lecture Notes in Computer Science (LNCS), 10923
The objective of the work is the research and development of a web-based visualization system for the creation and testing of zoomable projection cards. The basic idea is to project a multidimensional data set onto two dimensions using projection methods to represent it on a 2D surface. Based on the Card, Mackinlay, and Shneiderman visualization pipeline, a data processing model has been developed. For data processing various distance metrics, dimension reduction methods, zooming approaches as well as presentation concepts are considered. The peculiarities and considerations of the respective technology are discussed. A zooming approach allows large amounts of data to be displayed on a limited area. In order to better visualize connections within the data, concepts of presentation are discussed. The data points are represented as glyph-based objects or using color maps, various shapes, and sizes. Best practices about colormaps are discussed. In order to display large amounts of data in real time, a separation of the generation and visualization process takes place. During generation, a tabular file and selected configuration execute computationally-intensive transformation processes to create map material. Similar to Google Maps, the generated map material is represented by a visualization. Management concepts for managing various map sets as well as their generation and presentation are presented. A user interface can be used to create and visualize map material. The user uploads a tabular file into the system and chooses between different configuration parameters. Subsequently, this information is used to generate map material. The maps and various interaction options are provided in the visualization interface. Using various application examples, the advantages of this visualization system are presented.
Visual Access to Performance Indicators in the Mining Sector
Eurographics / IEEE VGTC Conference on Visualization (EuroVis) - Short Papers
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.
Visual Interactive Creation and Validation of Text Clustering Workflows to Explore Document Collections
Visualization and Data Analysis 2017
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.
Definition und Visualisierung von zoombaren 2D-Projektionen im Web
Darmstadt, TU, Bachelor Thesis, 2016
Zielsetzung der Arbeit ist die Erforschung und Entwicklung eines web-basierten Visualisierungssystems zum Erstellen und Testen von zoombaren Projektionskarten. Die grundlegende Idee besteht darin einen multidimensionalen Datensatz mithilfe von Projektionsmethoden auf zwei Dimensionen zu projizieren, um sie auf einer 2D-Fläche darzustellen In Anlehnung an die Card, Mackinlay, Shneiderman Visualisierungspipeline ist ein Datenverarbeitungsmodell entwickelt worden. Für die Datenverarbeitung werden verschiedene Distanzmetriken, Dimensionsreduktionsverfahren, Zooming Ansätze sowie Darstellungskonzepte berücksichtigt. Die Besonderheiten sowie Überlegungen der jeweiligen Technologie werden diskutiert. Ein Zooming-Ansatz ermöglicht große Datenmengen auf einer begrenzten Fläche darzustellen. Um Zusammenhänge innerhalb der Daten besser zu visualisieren werden Darstellungskonzepte diskutiert. Die Datenpunkte werden als glyph-basierte Objekte oder mithilfe Colormaps, verschiedenen Formen und Größen dargestellt. Best-Practices über Colormaps werden diskutiert. Um große Datenmengen in Echtzeit darzustellen erfolgt eine Trennung von dem Generierungs- und Visualisierungsprozess. Bei der Generierung werden mithilfe einer tabellarischen Datei und gewählten Konfiguration rechenintensive Transformationsprozesse ausgeführt, um Kartenmaterial zu erzeugen. Ähnlich zu Google-Maps wird das erzeugte Kartenmaterial durch eine Visualisierung dargestellt. Managementkonzepte zur Verwaltung verschiedener Kartensets sowie deren Erzeugung und Darstellung werden präsentiert. Über eine Oberfläche kann der Benutzer Kartenmaterial erzeugen und visualisieren. Der Benutzer lädt eine tabellarische Datei ins System hoch und wählt zwischen verschiedenen Konfigurationsparametern. Anschließend werden diese Informationen verwendet um Kartenmaterial zu erzeugen. Das Kartenmaterial sowie verschiedene Interaktionsmöglichkeiten werden in der Visualisierungsoberfläche bereitgestellt. Anhand verschiedener Anwendungsbeispiele werden die Vorteile dieses Visualisierungssystems präsentiert.
Supporting Collaborative Political Decision Making - An Interactive Policy Process Visualization System
VINCI 2016. The 9th International Symposium on Visual Information Communication and Interaction
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.
Visual-Interactive Document Classification
Darmstadt, TU, Master Thesis, 2016
In recent years, textual data are stored in a web or document format. These data are stored in an unstructured manner. In Organizing these data in some structured format would help exploiting these information in many applications. However, this task or organizing requires lots of time and human efforts. The Machine can help user to organize these data. User can define some labels and manually assign some documents to these classes. With these manually assigned documents, the machine can build a model that will automatically classify rest of the documents, which have not been labeled by the user. Though machine also requires some human effort to assign label to some documents based on which it can decide to classify rest of the documents. Again, this task requires lots of human effort and it is time consuming too, as human requires some time to manually assign label to documents. The problem is how to get good classification result with less human effort or in other words, in a situation where the user is only required to assign label to only few documents. So, in this thesis, an application is built with user interface to provide the results to the user in an interactive manner. The application takes some input from the user, build a model and then presents results using visualization techniques. Along with the results, suggestions are also provided to the user for the next iteration to build more accurate classifier. A process called Active Learning helps user to build more accurate model. Active learning is a process in which a classifier will interactively query the user to get some desired results on new samples. Active Learning helps users to pick up the most informative sample/document for classification. It provides suggestions to the user to select best document for training. In this thesis, an approach for classifying documents using user's feedback is introduced. Classification results will be provided to the users in an visualization manner. For the next iterations, users are presented with suggestions to pick up the most informative document. This task is done using Active Learning approach. By using the suggestions, user can give his own feedback and help the classifier to build a model which is more accurate. These suggestions are provided to the user in visualization manner. Users will be provided confidence score and colors of the desired class in Graphical User Interface(GUI). Thus, user can easily select the next document as suggested by the model. In this way, overall performance of the classifier will be improved. The model is evaluated using visualization of results; and using some results of consecutive iterations, more accurate model was achieved.
Visuell-Interaktive Exploration von Text Clustering Ergebnissen
Darmstadt, TU, Master Thesis, 2016
Digital vorliegende Texte gewinnen immer mehr an Bedeutung und sind gleichzeitig in ihrer Vielzahl und Komplexität für einen Leser nur schwer zu durchschauen. Clustering-Verfahren können hier hilfreich sein: Sie unterstützen den Anwender dabei, Text auf Basis des enthaltenen Inhalts zu gruppieren. Allerdings bestehen dabei viele Abhängigkeiten, die zu potenziell sehr diversen Ergebnissen führen, wobei es immer vom konkreten Einzelfall abhängt, welches Ergebnis als "das beste" anzusehen ist. Deswegen ist es sowohl sinnvoll, mehrere Clusterings mit unterschiedlichen Parametern durchzuführen und zu vergleichen, als auch, den Benutzer aktiv in den Analyseprozess miteinzubeziehen. Ziel dieser Arbeit war es, ein Textclustering-System zu entwickeln, das in der Lage ist, Clusterings mit direkter Interaktion des Benutzers zu erstellen, zu analysieren und zu vergleichen. Hierfür wurde sowohl eine neuartige Version der Featureselektion implementiert als auch sehr viel Wert auf die Visualisierung der einzelnen Prozessabläufe gelegt. Eine anschließende Auswertung kam zu dem Ergebnis, dass die Featureselektion gut funktioniert und die Nützlichkeit des Systems gegeben ist. Für die Zukunft bietet es sich an, die Verfahren im System noch zu erweitern und dem Benutzer die Möglichkeit zu geben, selbst weitere Qualitätsmetriken und Verfahren einzupflegen.
The "EU Community" Project - Coupling the Power of Data with Community Expertise
EEPM 2015 Enabling Effective Policy Making
Workshop on Enabling Effective Policy Making (EEPM) <2015, Thessaloniki, Greece>
The EU Community project seeks to promote, facilitate, and ultimately exploit the synergy of a cutting-edge intelligent collaboration platform with a community of institutional actors, stakeholders, scientists, consultants, media analysts and other individuals that can make valuable contributions to EU policy debates. Its ultimate goal is to effectuate a transformation in the modus operandi of EU politics and move closer to achieving the illusive goals of improved transparency, efficiency, awareness and engagement, ultimately leading to better policies for a better European Union.
VisInfo: A Digital Library System for Time Series Research Data Based on Exploratory Search - a User-centered Design Approach
International Journal on Digital Libraries
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.
Visual Analytics of Work Behavior Data - Insights on Individual Differences
Eurographics Conference on Visualization (EuroVis) - Short Papers
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.
Visual Decision Support for Policy Making: Advancing Policy Analysis with Visualization
Policy Practice and Digital Science
Today's politicians are confronted with new information technologies to tackle complex decision-making problems. In order to make sustainable decisions, a profound analysis of societal problems and possible solutions (policy options) needs to be performed. In this policy-analysis process, different stakeholders are involved. Besides internal direct advisors of the policy makers (policy analysts), external experts from different scientific disciplines can support evidence-based decision making. Despite the alleged importance of scientific advice in the policy-making process, it is observed that scientific results are often not used. In this work, a concept is described that supports the collaboration between scientists and politicians. We propose a science-policy interface that is realized by including information visualization in the policy-analysis process. Therefore, we identify synergy effects between both fields and introduce a methodology for addressing the current challenges of science-policy interfaces with visualization. Finally, we describe three exemplary case studies carried out in European research projects that instantiate the concept of this approach.
Visual-Interactive Text Analysis to Support Political Decision Making - From Sentiments to Arguments to Policies
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.
Combining Computational Models and Interactive Visualization to Support Rational Decision Making
Advances in Visual Computing. 10th International Symposium, ISVC 2014
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.
Towards a Tighter Coupling of Visualization and Public Policy Making
IEEE Conference on Visual Analytics Science and Technology. Proceedings
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.
User-Based Visual-Interactive Similarity Definition for Mixed Data Objects - Concept and First Implementation
WSCG 2014. Communication Papers Proceedings
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.
Visual Access to an Agent-based Simulation Model to Support Political Decision Making
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.
Bridging Knowledge Gaps in Policy Analysis with Information Visualization
Electronic Government and Electronic Participation
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.
Definition eines Evaluierungsprozesses für Visual-Analytics-Expertenlösungen
Darmstadt, TU, Studienarbeit, 2013
Heutzutage werden riesige Datenmengen produziert. Es ist wichtig diese Daten interpretieren zu können. Visual Analytics Systeme helfen dabei, große Datenbestände nutzbar zu machen. Die Systeme können sich je nach Einsatzgebiet stark unterscheiden. Damit Benutzer produktiv mit den Systemen arbeiten können, ist es wichtig, dass die Systeme auf unterschiedliche Kriterien wie Benutzbarkeit, Effizienz, Robustheit oder Angemessenheit evaluiert werden. In dieser Arbeit werden Modelle zum Thema Evaluierung von Visual Analytics Systemen vorgestellt. Die vorgeschlagenen Methoden und Heuristiken werden untersucht und bewertet. Aus den gewonnen Erkenntnissen werden schließlich praxisorientierte Richtlinien abgeleitet, die helfen sollen, Systeme in Zukunft systematischer auszuwerten. Am Ende der Arbeit wird ein exemplarischer Projektablauf vorgestellt an dem die Richtlinien angewandt werden.
Visual Access to Optimization Problems in Strategic Environmental Assessment
Advances in Visual Computing. 9th International Symposium, ISVC 2013
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.
Visual Analysis of Multidimensional Optimization Problems
Darmstadt, TU, Bachelor Thesis, 2013
This work presents a visual interface to access an optimization system to solve multidimensional decision problems. Today the complexity of decision making problems is generally high because many aspects have to be considered. Especially in the policy making process substantial decisions require profound knowledge. Strategic environmental assessment plays an important role in this process. The duty to consider environmental impacts caused by political decisions is obliged by law in many countries. This makes the decision making process more complex and requires better methods to find a solution. With optimization it is possible to create mathematical models that weight out multidimensional decisions. The models and the produced solutions have to be reviewed by the decision makers but they are not easy-to-understand. In my approach I design and implement a visual-interactive application that enables the visual access to complex optimization models, and the analysis of alternative solutions. It makes use of approved visualization techniques and state of the art methodologies to make abstract information transparent. This conveys the knowledge behind the decision options in a reasonable way. Contributions are a visual-interactive interface that enables the visual access to complex optimization models, and the analysis of alternative solutions. In this way strategic environmental assessment can be improved and the policy maker is able to understand where policy options originated. The evaluation of the prototype revealed that the interface offers an easy access to alternative solutions and gives more insight to the process behind finding policy options.
Visualizing Uncertain Underground Information for Urban Management
Proceedings of the IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing
IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP) <2013, Prague, Czech Republic>
In this paper we present approaches for visualizing uncertainty in an application context for urban management. We describe techniques for the visualization of uncertainty and methods for the reduction of the complexity of the visualization to avoid cognitive overload. Uncertainty in both natural and man-made structures in the underground is thus communicated to the user in an appropriate, non-threatening manner. The methods were evaluated during an end-user workshop. The results of this workshop have led to various extensions to the current approach to the visualization of uncertainty in urban management.
Content-Based Layouts for Exploratory Metadata Search in Scientific Research Data
JCDL 2012. Proceedings
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.
Guided Discovery of Interesting Relationships Between Time Series Clusters and Metadata Properties
i-KNOW '12. Proceedings
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.
Toward Visualization in Policy Modeling
IEEE Computer Graphics and Applications
Information visualization and visual analytics have become widely recognized research fields applied to a variety of domains and data-related challenges. This development's main driver has been the rapidly increasing amount of data that must be dealt with daily. At the same time, citizens, shareholders, and customers expect highly efficient, informed decision-making based on increasingly complex, dynamic, and interdependent data and information. All this applies in many ways to public-policy modeling. As the recent financial crisis has shown, policy making and regulation are highly challenging tasks. The outcomes of policy choices and individual behavior aren't easily predictable in our complex society. Ubiquitous computing, crowd sourcing, and open data, to name just a few examples, are creating masses of data that governments struggle to make sense of for policy modeling. Increasingly, policy makers are perceiving visualization and data analysis as critical to this sense-making process. This article examines the current and future roles of information visualization, semantics visualization, and VA in policy modeling.
Towards Process-Oriented Information Visualization for Supporting Users
International Conference on Interactive Collaborative Learning (ICL) <15, 2012, Villach, Austria>
Nowadays daily office work consists of dealing with big numbers of data and data sources, and furthermore of working with complex computer programs. In consequence many users have problems to use such programs effective and efficient. In particular beginners have significant problems to use the programs correctly due to complex functionality and interaction options. To avoid this overload of the user, the Information Visualization community has recently developed some approaches that aim to support the users. Unfortunately, these approaches are limited to one special aspect, and sometimes they are just appropriate for one special task. Thus, in this paper we introduce a process-oriented user-supporting approach. It allows selecting adequate supporting techniques in correlation to a performed process and activity to guide the user and help him to solve his task. Furthermore, we show the benefits of designing programs and applications, which implement process definitions for the existing tasks to provide the user with better process orientation. This guides the user through difficult and complex processes.
Visual Exploration of Local Interest Points in Sets of Time Series
IEEE Conference on Visual Analytics Science and Technology 2012. Proceedings
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.
Visual-Interactive Preprocessing of Time Series Data
Proceedings of SIGRAD 2012
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.
A Visual Digital Library Approach for Time-Oriented Scientific Primary Data
International Journal on Digital Libraries
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.
Guiding Feature Subset Selection with an Interactive Visualization
IEEE Conference on Visual Analytics Science and Technology 2011. Proceedings
IEEE Symposium on Visual Analytics Science and Technology (VAST) <6, 2011, Providence, RI, USA>
We propose a method for the semi-automated refinement of the results of feature subset selection algorithms. Feature subset selection is a preliminary step in data analysis which identifies the most useful subset of features (columns) in a data table. So-called filter techniques use statistical ranking measures for the correlation of features. Usually a measure is applied to all entities (rows) of a data table. However, the differing contributions of subsets of data entities are masked by statistical aggregation. Feature and entity subset selection are, thus, highly interdependent. Due to the difficulty in visualizing a high-dimensional data table, most feature subset selection algorithms are applied as a black box at the outset of an analysis. Our visualization technique, SmartStripes, allows users to step into the feature subset selection process. It enables the investigation of dependencies and interdependencies between different feature and entity subsets. A user may even choose to control the iterations manually, taking into account the ranking measures, the contributions of different entity subsets, as well as the semantics of the features.
SmartStripes - Looking under the Hood of Feature Subset Selection Methods
International Workshop on Visual Analytics (EuroVA) <2, 2011, Bergen, Norway>
We propose a visualization method for the diagnosis and interactive refinement of automatic techniques for feature subset selection. So-called filter techniques use statistical ranking measures to identify the most useful combination of features for further analysis. Usually a measure is applied to all entities of a data-table. The influence of atypical entities can distort the result, but this distortion may be masked by the statistical aggregation. Clearly, feature and entity subset selection are highly interdependent. Our technique, SmartStripes, intends to make this interdependency visible.
Tackling Uncertainty in Combined Visualizations of Underground Information and 3D City Models
GeoViz Hamburg 2011 Workshop
GeoViz Workshop <2, 2011, Hamburg, Germany>
Cities are under constant development. They are characterized not only by their surface constructions like buildings and traffic infrastructure, but also by their underground structures. Besides human-created lifelines, tunnels and quarries, there are also diverse geological formations. Underground information contains a lot of uncertainty by nature, because measurements provide information along drilling lines only. Additionally, man-made structures are often hardly documented. In this paper we will present ways to visualize such uncertainty in combination with exact surface structures from 3D city models in order to assist stakeholders in making decisions. We will evaluate existing techniques and describe the requirements imposed on uncertainty visualization.
Visualizing Uncertain Underground Information for Urban Management
Working with Uncertainty Workshop <1, 2011, Providence, Rhode Island>
We present approaches for visualizing uncertainty in an application context through techniques for the visualization of uncertainty. We also describe methods for the reduction of the complexity of the visualization to avoid cognitive overload. Uncertainty in both natural and man-made structures under ground is thus communicated to the user in an appropriate, non threatening manner. The methods were evaluated during an end-user workshop of the research project DeepCity3D. The results of this workshop have led to various extensions to our uncertainty visualization approach in urban management
A Radial Visualization Tool for Depicting Hierarchically Structured Video Content
IEEE Conference on Visual Analytics Science and Technology 2010. Proceedings
IEEE Symposium on Visual Analytics Science and Technology (VAST) <5, 2010, Salt Lake City, UT, USA>
The visual analysis of video content is an important research topic due to the huge amount of video data that is generated every day. Annotating this data will become a major problem since the amount of videos further increases. With this work we introduce a system that combines a visualization tool with automatic video segmentation techniques and a characteristic key-frame extraction. A summary of the content of a whole video in one view is realized. Furthermore, the user can interactively browse through the video via our visualization interface to get more detailed information. The system is adapted to two application scenarios and a third application is discussed for future work.
A Visual Digital Library Approach for Time-Oriented Scientific Primary Data
Research and Advanced Technology for Digital Libraries
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.
DeepCity3D: Integration von 3D-Stadtmodellen und Untergrundinformationen
Geoinformatik 2010. Konferenzband
Geoinformatik-Tagung <2010, Kiel, Germany>
Modern cities are under constant development. They are characterized not only by their surface constructions like buildings and traffic infrastructure, but also by their underground structures. Besides human-created lifelines, tunnels and quarries, there are also diverse geological formations. All this information is important for a sustainable urban planning and utility management. The DeepCity3D project therefore aims to develop application-adaptive 3D visualization tools that integrate underground data and city models (provided in standardized formats) with advanced functionality to support decision making for stakeholders involved in urban planning, construction companies, insurance companies, architecture, or environmental protection.
Information Visualisation and Visual Analytics for Governance and Policy Modelling
Future Research on ICT for Governance and Policy Modelling
Gap Analyses & Roadmap Validation Workshop <2010, Samos, Greece>
Professionals involved in governance and policy modelling have seen a dramatic increase in the volume of potentially relevant data. As in many other knowledge-based fields today, policy makers face the problem of an information overload. Putting data from sources as diverse as blogs, online opinion polls and government reports to effective use is a task which requires new tools for their analysis and visualisation. In this paper, we introduce the research field known as Visual Analytics and we argue that it can help policy makers integrate large amounts of heterogeneous data into their decisionmaking processes. In so doing, Visual Analytics has the potential to increase not only the relevance, but also the legitimacy of policy decisions. In addition, policy modelling could provide the Visual Analytics community with new research challenges.
Visual Analytics - Verbindung von Analyseverfahren und Visualisierungstechniken
IM - Die Fachzeitschrift für Information Management & Consulting
Visual Analytics bietet eine Möglichkeit, um große Datenmengen besser zu verstehen. Wissen kann damit aus den gesammelten Unternehmensdaten gewonnen werden. Dieses Wissen kann für Unternehmen einen Wettbewerbsvorteil bedeuten. Bisher werden Unternehmenskennzahlen lediglich mithilfe von statistischen Modellen berechnet. Doch der Bezug zwischen den Daten, den Modellen und den Unternehmenszielen bleibt häufig unklar. Wenn die Visualisierung als wesentlicher Bestandteil des Analyseprozesses eingebunden wird, wird das Verständnis von den Modellen sowie auch von den Daten erhöht. Fehler in den zugrundeliegenden Annahmen können früher behoben werden und neu entdeckte Zusammenhänge können zu sicheren, an Fakten und Zielen überprüfbaren Kennzahlen beim Reporting führen.
Visuelle Analysen des Datensatzes: Wie versteckte Zusammenhänge sichtbar werden
Allgemeinbildung in Deutschland
Wenn Wissenschaftler Daten (z.B. des Studentenpisa-Tests des SPIEGEL) analysieren, stellen sie gemeinhin Hypothesen auf und überprüfen diese dann. In diesem Beitrag wird ein anderes Verfahren vorgestellt. Es handelt sich um ein exploratives Vorgehen, das es erlaubt, versteckte Zusammenhänge in großen und komplexen Datensammlungen zu finden. Dazu werden die Daten ohne die Formulierung einer bestimmten Fragestellung mittels interaktiver graphischer Darstellungen untersucht. Der Beitrag erläutert diese Herangehensweise und stellt drei Techniken vor, die für diesen Zweck am Fraunhofer-Institut für Graphische Datenverarbeitung in Darmstadt und an der TU Darmstadt entwickelt worden sind. Die Vielzahl von möglichen Aussagen über einen unbekannten Datensatz wird mit diesen und vergleichbaren Techniken auf jene reduziert, die es wert sind, genauer untersucht zu werden.