Interactive Visual Analytics and Visualization for Decision Making – Making Sense of a Growing Digital World

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https://hdl.handle.net/10125/63618

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    Route Packing: Geospatially-Accurate Visualization of Route Networks
    ( 2020-01-07) Zhao, Jieqiong ; Karimzadeh, Morteza ; Xu, Hanye ; Malik, Abish ; Afzal, Shehzad ; Wang, Guizhen ; Elmqvist, Niklas ; Ebert, David
    We present route packing}, a novel (geo)visualization technique for displaying several routes simultaneously on a geographic map while preserving the geospatial layout, identity, directionality, and volume of individual routes. The technique collects variable-width route lines side by side while minimizing crossings, encodes them with categorical colors, and decorates them with glyphs to show their directions. Furthermore, nodes representing sources and sinks use glyphs to indicate whether routes stop at the node or merely pass through it. We conducted a crowd-sourced user study investigating route tracing performance with road networks visualized using our route packing technique. Our findings highlight the visual parameters under which the technique yields optimal performance.
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    Comparison of Attention Behaviour Across User Sets through Automatic Identification of Common Areas of Interest
    ( 2020-01-07) Muthumanickam, Prithiviraj ; Helske, Jouni ; Nordman, Aida ; Johansson, Jimmy ; Cooper, Matthew
    Eye tracking is used to analyze and compare user behaviour across diverse domains, but long duration eye tracking experiments across multiple users generate millions of eye gaze samples, making the data analysis process complex. Usually the samples are labelled into Areas of Interest (AoI) or Objects of Interest (OoI), where the AoI approach aims to understand how a user monitors different regions of a scene, while OoI identification uncovers distinct objects in the scene that attract user attention. Using scalable clustering and cluster merging that is not constrained by input parameters, we label AoIs across multiple users in long duration eye tracking experiments. Using the common AoI labels then allows direct comparison of the users as well as the use of such methods as Hidden Markov Models and Sequence mining to uncover interesting behaviour across the users which, until now, has been prohibitively difficult to achieve.
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    The Use of Embedded Interaction Mechanisms for Low-Level Analysis Tasks
    ( 2020-01-07) Sandouka, Kari ; Noteboom, Cherie
    The use of information visualization is a strategy to reduce information overload and cognitive efforts. Interaction mechanisms aid the exploration of data when it is not practical to display all data points in one visual display. This study reports the results of a pilot study. The purpose of the study is to determine what interactive mechanisms are used and how they support a task or set of tasks.
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    Visualization and Interaction for Knowledge Discovery in Simulation Data
    ( 2020-01-07) Feldkamp, Niclas ; Bergmann, Sören ; Strassburger, Steffen
    Discrete-event simulation is an established and popular technology for investigating the dynamic behavior of complex manufacturing and logistics systems. Besides traditional simulation studies that focus on single model aspects, data farming describes an approach for using the simulation model as a data generator for broad scale experimentation with a broader coverage of the system behavior. On top of that we developed a process called knowledge discovery in simulation data that enhances the data farming concept by using data mining methods for the data analysis. In order to uncover patterns and causal relationships in the model, a visually guided analysis then enables an exploratory data analysis. While our previous work mainly focused on the application of suitable data mining methods, we address suitable visualization and interaction methods in this paper. We present those in a conceptual framework followed by an exemplary demonstration in an academic case study.
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    Visual Analysis for Spatio-Temporal Event Correlation in Manufacturing
    ( 2020-01-07) Herr, Dominik ; Kurzhals, Kuno ; Ertl, Thomas
    The analysis of events with spatio-temporal context and their interdependencies is a crucial task in the manufacturing domain. In general, understanding this context, for example investigating error messages or alerts is important to take corrective actions. In the manufacturing domain, comprehending the relations of errors is often based on the technicians' experience. Validation of cause-effect relations is necessary to understand if an effect has a preceding causality, e.g., if an error is the result of multiple issues from previous working steps. We present an approach to investigate spatio-temporal relations between such events. Based on a time-sensitive correlation measure, we provide multiple coordinated views to analyze and filter the data. In collaboration with an industry partner, we developed a visual analytics approach for error logs reported by machines that covers a multitude of analysis tasks. We present a case study based on real-world event logs of an assembly line with feedback from our industry partner's domain experts. The findings show that experts can effectively identify error dependencies that impair the overall assembly line productivity using our technique. Furthermore, we discuss how our approach is applicable in other domains.
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    Industrial Production Process Improvement by a Process Engine Visual Analytics Dashboard
    ( 2020-01-07) Suschnigg, Josef ; Ziessler, Florian ; Brillinger, Markus ; Vukovic, Matej ; Mangler, Jürgen ; Schreck, Tobias ; Thalmann, Stefan
    Digitalization reshapes production in a sense that production processes are required to be more flexible and more interconnected to produce products in smaller lot sizes. This makes the process improvement much more challenging, as traditional approaches, which are based on the learning curve, are difficult to apply. Data-driven technologies promise help in learning faster by making use of the massive data volumes collected in production environments. Visual analytics approaches are particularly promising in this regard as they aim to enable engineers with their rich domain knowledge to identify opportunities for process improvements. Based on the assumption that process improvement should be connected with the process engine managing the process execution, we propose a visual analytics dashboard which integrates process models. Based on a case study in the smart factory of Vienna, we conducted two pair analytics sessions. The first results seem promising, whereas domain experts articulate their wish for improvements and future work.
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