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

Using Dashboard Networks to Visualize Multiple Patient Histories: A Design Study on Post-operative Prostate Cancer

In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or treatment combinations, and then visualizes the aggregated segments as static dashboards that are arranged in a dashboard network to show longitudinal changes. The static dashboards were developed in nine iterations, to show 15 important attributes from the patients' histories. The final design was evaluated with five non-experts, five visualization experts and four medical experts, who successfully used it to gain an overview of a 2,000 patient dataset, and to make observations about longitudinal changes and differences between two cohorts. The research represents a step-change in the detail of large-scale data that may be successfully visualized using dashboards, and provides guidance about how the approach may be generalized

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

Intelligent visualisation and information presentation for civil crisis management

This paper describes an ongoing research work on developing methods for effective visualisation support for situation analysis, decision making, and communication in the course of disaster management. The major goals are to reduce the information load of the analyst, decision maker, or information recipient without omission of anything important and to ensure quick and accurate comprehending of the information. The work embraces the issues of selection of the relevant information and defining the appropriate level of detail, data preparation (aggregation and other transformations), and selection of the appropriate methods for visual representation depending on the user's tasks or communication goals, recipient's profile, and the target presentation medium. A practical outcome from the research will be a knowledge base that can be used to support analysis, decision making, and information communication in emergency situations. A great part of the knowledge, specifically, knowledge on data transformation and representation, is generic and can be used for different applications

Methods and a research agenda for the evaluation of event sequence visualization techniques

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

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

PAVED: Pareto Front Visualization for Engineering Design

Design problems in engineering typically involve a large solution space and several potentially conflicting criteria. Selecting a compromise solution is often supported by optimization algorithms that compute hundreds of Pareto‐optimal solutions, thus informing a decision by the engineer. However, the complexity of evaluating and comparing alternatives increases with the number of criteria that need to be considered at the same time. We present a design study on Pareto front visualization to support engineers in applying their expertise and subjective preferences for selection of the most‐preferred solution. We provide a characterization of data and tasks from the parametric design of electric motors. The requirements identified were the basis for our development of PAVED, an interactive parallel coordinates visualization for exploration of multi‐criteria alternatives. We reflect on our user‐centered design process that included iterative refinement with real data in close collaboration with a domain expert as well as a summative evaluation in the field. The results suggest a high usability of our visualization as part of a real‐world engineering design workflow. Our lessons learned can serve as guidance to future visualization developers targeting multi‐criteria optimization problems in engineering design or alternative domains

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