Browsing by Author "Elzen, Stef van den"

Now showing 1 - 3 of 3
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    ChronoCorrelator: Enriching Events with Time Series
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) van Dortmont, Martijn; Elzen, Stef van den; Wijk, Jarke J. van; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Event sequences and time series are widely recorded in many application domains; examples are stock market prices, electronic health records, server operation and performance logs. Common goals for recording are monitoring, root cause analysis and predictive analytics. Current analysis methods generally focus on the exploration of either event sequences or time series. However, deeper insights are gained by combining both. We present a visual analytics approach where users can explore both time series and event data simultaneously, combining visualization, automated methods and human interaction. We enable users to iteratively refine the visualization. Correlations between event sequences and time series can be found by means of an interactive algorithm, which also computes the presence of monotonic effects. We illustrate the effectiveness of our method by applying it to real world and synthetic data sets.
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    FlexEvent: going beyond Case-Centric Exploration and Analysis of Multivariate Event Sequences
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Linden, Sanne van der; Wulterkens, Bernice M.; Gilst, Merel M. van; Overeem, Sebastiaan; Pul, Carola van; Vilanova, Anna; Elzen, Stef van den; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
    In many domains, multivariate event sequence data is collected focused around an entity (the case). Typically, each event has multiple attributes, for example, in healthcare a patient has events such as hospitalization, medication, and surgery. In addition to the multivariate events, also the case (a specific attribute, e.g., patient) has associated multivariate data (e.g., age, gender, weight). Current work typically only visualizes one attribute per event (label) in the event sequences. As a consequence, events can only be explored from a predefined case-centric perspective. However, to find complex relations from multiple perspectives (e.g., from different case definitions, such as doctor), users also need an event- and attribute-centric perspective. In addition, support is needed to effortlessly switch between and within perspectives. To support such a rich exploration, we present FlexEvent: an exploration and analysis method that enables investigation beyond a fixed case-centric perspective. Based on an adaptation of existing visualization techniques, such as scatterplots and juxtaposed small multiples, we enable flexible switching between different perspectives to explore the multivariate event sequence data needed to answer multi-perspective hypotheses. We evaluated FlexEvent with three domain experts in two use cases with sleep disorder and neonatal ICU data that show our method facilitates experts in exploring and analyzing real-world multivariate sequence data from different perspectives.
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    ModelWise: Interactive Model Comparison for Model Diagnosis, Improvement and Selection
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Meng, Linhao; Elzen, Stef van den; Vilanova, Anna; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Model comparison is an important process to facilitate model diagnosis, improvement, and selection when multiple models are developed for a classification task. It involves careful comparison concerning model performance and interpretation. Current visual analytics solutions often ignore the feature selection process. They either do not support detailed analysis of multiple multi-class classifiers or rely on feature analysis alone to interpret model results. Understanding how different models make classification decisions, especially classification disagreements of the same instances, requires a deeper model understanding. We present ModelWise, a visual analytics method to compare multiple multi-class classifiers in terms of model performance, feature space, and model explanation. ModelWise adapts visualizations with rich interactions to support multiple workflows to achieve model diagnosis, improvement, and selection. It considers feature subspaces generated for use in different models and improves model understanding by model explanation. We demonstrate the usability of ModelWise with two case studies, one with a small exemplar dataset and another developed with a machine learning expert with real-world perioperative data.