AN INTERACTIVE REMOTE VISUALIZATION SYSTEM FOR MOBILE APPLICATION ACCESS

This paper introduces a remote visualization approach that enables the visualization of data sets on mobile devices or in web environments. With this approach the necessary computing power can be outsourced to a server environment. The developed system allows the rendering of 2D and 3D graphics on mobile phones or web browsers with high quality independent of the size of the original data set. Compared to known terminal server or other proprietary remote systems our approach offers a very simple way to integrate with a large variety of applications which makes it useful for real-life application scenarios in business processes

Visual Firewall Log Analysis -- At the Border Between Analytical and Appealing

In this paper, we present our design study on developing an interactive visual firewall log analysis system in collaboration with an IT service provider. We describe the human-centered design process, in which we additionally considered hedonic qualities by including the usage of personas, psychological need cards and interaction vocabulary. For the problem characterization we especially focus on the demands of the two main clusters of requirements: high-level overview and low-level analysis, represented by the two defined personas, namely information security officer and network analyst. This resulted in the prototype of a visual analysis system consisting of two interlinked parts. One part addresses the needs for rather strategical tasks while also fulfilling the need for an appealing appearance and interaction. The other part rather addresses the requirements for operational tasks and aims to provide a high level of flexibility. We describe our design journey, the derived domain tasks and task abstractions as well as our visual design decisions, and present our final prototypes based on a usage scenario. We also report on our capstone event, where we conducted an observed experiment and collected feedback from the information security officer. Finally, as a reflection, we propose the extension of a widely used design study process with a track for an additional focus on hedonic qualities

Extension of Dictionary-Based Compression Algorithms for the Quantitative Visualization of Patterns from Log Files

Many services today massively and continuously produce log files of different and varying formats. These logs are important since they contain information about the application activities, which is necessary for improvements by analyzing the behavior and maintaining the security and stability of the system. It is a common practice to store log files in a compressed form to reduce the sheer size of these files. A compression algorithm identifies frequent patterns in a log file to remove redundant information. This work presents an approach to detect frequent patterns in textual data that can be simultaneously registered during the file compression process with low consumption of resources. The log file can be visualized with the possibility to explore the extracted patterns using metrics based on such properties as frequency, length and root prefixes of the acquired pattern. This allows an analyst to gain the relevant insights more efficiently reducing the need for manual labor-intensive inspection in the log data. The extension of the implemented dictionary-based compression algorithm has the advantage of recognizing patterns in log files of any format and eliminates the need to manually perform preparation for any preprocessing of log files.Comment: submitted to EuroVA 202

Towards medhub: A Self-Service Platform for Analysts and Physicians

Combining clinical and omics data can improve both daily clinical routines and research to gain more insights into complex medical procedures. We present the results of our first phase in a multi-year collaboration with analysts and physicians aiming at improved inter-disciplinary biomarker identification. We also outline our user-centered approach along its challenges, describe the intermediate technical artifacts that serve as a basis for summative and formative evaluation for the second project phase. Finally, we sketch the road ahead and how we intend to combine visualization research with user-centered design through problem-based prioritization.Comment: 2 + 1 page

Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Features

Deep learning (DL) models achieve remarkable performance in classification tasks. However, models with high complexity can not be used in many risk-sensitive applications unless a comprehensible explanation is presented. Explainable artificial intelligence (xAI) focuses on the research to explain the decision-making of AI systems like DL. We extend a recent method of Class Activation Maps (CAMs) which visualizes the importance of each feature of a data sample contributing to the classification. In this paper, we aggregate CAMs from multiple samples to show a global explanation of the classification for semantically structured data. The aggregation allows the analyst to make sophisticated assumptions and analyze them with further drill-down visualizations. Our visual representation for the global CAM illustrates the impact of each feature with a square glyph containing two indicators. The color of the square indicates the classification impact of this feature. The size of the filled square describes the variability of the impact between single samples. For interesting features that require further analysis, a detailed view is necessary that provides the distribution of these values. We propose an interactive histogram to filter samples and refine the CAM to show relevant samples only. Our approach allows an analyst to detect important features of high-dimensional data and derive adjustments to the AI model based on our global explanation visualization.Comment: submitted to xaiworldconference202

Supporting Domain Characterization in Visualization Design Studies With the Critical Decision Method

While domain characterization has become an integral part of visualization design studies, methodological prescriptions are rare. An underrepresented aspect in existing approaches is domain expertise. Knowledge elicitation methods from cognitive science might help but have not yet received much attention for domain characterization. We propose the Critical Decision Method (CDM) to the visualization domain to provide descriptive steps that open up a knowledge-based perspective on domain characterization. The CDM uses retrospective interviews to reveal expert judgment involved in a challenging situation. We apply it to study three domain problems, reflect on our practical experience, and discuss its relevance to domain characterization in visualization research. We found the CDM's realism and subjective nature to be well suited for eliciting cognitive aspects of high-level task performance. Our insights might guide other researchers in conducting domain characterization with a focus on domain knowledge and cognition. With our work, we hope to contribute to the portfolio of meaningful methods used to inform visualization design and to stimulate discussions regarding prescriptive steps for domain characterization

Knowledge Representation for Decision-Centered Visualization

Users of information systems in timecritical domains are under pressure to digest and process information that is vital for their task. Too often analysts receive the important information buried within a collection of insignificant data and information. Sorting through such a cluttered display and finding the critical information requires a high level of attention and becomes even more challenging with the increasing amounts of information available. Especially in timecritical domains, the problem of information assessment is replaced by the problem of avoiding the timeintensive efforts to review all the available information. The focus of this dissertation was the combination of knowledge representation, information visualization, and human-centered computing to reduce the probability of such information overload, defining the field of decision-centered visualization (DCV). The DCV approach is unique in how it ties together information visualization and knowledge representation in time-critical and information-intensive environments to support the user's situation awareness. The knowledge representation for DCV as it is defined in this thesis corresponds to the conceptual and architectural requirements of DCV to enable decision- centered applications. The goal of this work was a knowledge representation approach that is user-centered, allows scalable information handling, supports knowledge- based information visualization, flexibly supports various kinds of display and presentation systems, and can be realized in a dynamic network environment - all aspects of information systems typically encountered in time-critical domains like air traffic control. Part of the solution introduced in this work was the scalable combination of domain ontologies and domain databases for human-centered and knowledge-based information visualization. The domain ontology as the knowledge structuring element of the representation is integrated with the scalability advantages of databases, together representing the visualization-relevant aspects of a domain. This thesis extends the current information visualization theory by socalled presentation requirements (PRs). PRs allow the abstract analytical description of information, particularly for the support of situation awareness. By describing entity types, their attributes, and relationships between entity types, knowledge about the information in the domain can be combined with knowledge about the necessary information visualization to users under time pressure. This is enabled by using presentation types and data types for the description of information visualization environments, leaning on and extending the current information visualization models with aspects for time-critical and information-intensive domains. With the help of so-called DCV transformations, this work enables the flexible guidance of various display systems by a DCV system tuning the visualization to the current situation, to the task that the user is working working on, and to the role of the user in the current situation. Depending on the needs of the user and the used display system, filtered and prioritized information can be flexibly mapped to visual structures and presented in an effective and efficient way. The results of this work are most applicable to the area of emergency management and are also applied to the field of visual analytics, an integrated field between data mining and information visualization in the area of very information-intensive domains. Here, the presentation requirements enable the reduction of the complexity of the information on an abstract level based on the provided semantics, before any data instances have to be considered

Knowledge Representation for Decision-Centered Visualization

Users of information systems in timecritical domains are under pressure to digest and process information that is vital for their task. Too often analysts receive the important information buried within a collection of insignificant data and information. Sorting through such a cluttered display and finding the critical information requires a high level of attention and becomes even more challenging with the increasing amounts of information available. Especially in timecritical domains, the problem of information assessment is replaced by the problem of avoiding the timeintensive efforts to review all the available information. The focus of this dissertation was the combination of knowledge representation, information visualization, and human-centered computing to reduce the probability of such information overload, defining the field of decision-centered visualization (DCV). The DCV approach is unique in how it ties together information visualization and knowledge representation in time-critical and information-intensive environments to support the user's situation awareness. The knowledge representation for DCV as it is defined in this thesis corresponds to the conceptual and architectural requirements of DCV to enable decision- centered applications. The goal of this work was a knowledge representation approach that is user-centered, allows scalable information handling, supports knowledge- based information visualization, flexibly supports various kinds of display and presentation systems, and can be realized in a dynamic network environment - all aspects of information systems typically encountered in time-critical domains like air traffic control. Part of the solution introduced in this work was the scalable combination of domain ontologies and domain databases for human-centered and knowledge-based information visualization. The domain ontology as the knowledge structuring element of the representation is integrated with the scalability advantages of databases, together representing the visualization-relevant aspects of a domain. This thesis extends the current information visualization theory by socalled presentation requirements (PRs). PRs allow the abstract analytical description of information, particularly for the support of situation awareness. By describing entity types, their attributes, and relationships between entity types, knowledge about the information in the domain can be combined with knowledge about the necessary information visualization to users under time pressure. This is enabled by using presentation types and data types for the description of information visualization environments, leaning on and extending the current information visualization models with aspects for time-critical and information-intensive domains. With the help of so-called DCV transformations, this work enables the flexible guidance of various display systems by a DCV system tuning the visualization to the current situation, to the task that the user is working working on, and to the role of the user in the current situation. Depending on the needs of the user and the used display system, filtered and prioritized information can be flexibly mapped to visual structures and presented in an effective and efficient way. The results of this work are most applicable to the area of emergency management and are also applied to the field of visual analytics, an integrated field between data mining and information visualization in the area of very information-intensive domains. Here, the presentation requirements enable the reduction of the complexity of the information on an abstract level based on the provided semantics, before any data instances have to be considered

Informationsvisualisierung und Visual Analytics

Die Visualisierung von Geschäftsdaten und großen Datenmengen für die Entscheidungsfindung nimmt einen immer wichtigeren Stellenwert ein. Für die effektive Nutzung heutiger Tools ist ein Grundverständnis der Visualisierung nötig. Das Wissen über die menschliche Wahrnehmung erlaubt es, grundlegende Herangehensweisen zum Aufbau effektiver Visualisierungen zu formulieren und zu nutzen. Die richtige Informationsvisualisierung von Daten für die richtigen Nutzer und Aufgaben beschleunigt das Erkennen und Verstehen dieser Daten immens. Massive Datenmengen benötigen erweiterte Ansätze zur Integration von Visualisierung und automatischen Datenverarbeitung. Diese Ansätze bezeichnen wir als Visual Analytics. Der Prozess des User-Centered Design stellt sicher, dass der Nutzer während Planung und Entwicklung von Visualisierungen im Zentrum des Interesses steht. Der Beitrag gibt einen Überblick über Grundlagen der Visualisierung auch für große Datenmengen. Zudem hebt er die wichtige Beachtung der Benutzer bei der Erstellung jeglicher Visualisierung hervor