EuroVA19

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https://diglib.eg.org/handle/10.2312/2632784

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Contextualized Analysis of Movement Events
(The Eurographics Association, 2019) Chen, Siming; Andrienko, Gennady; Andrienko, Natalia; Doulkeridis, Christos; Koumparos, Athanasios; Landesberger, Tatiana von and Turkay, Cagatay
For understanding the circumstances, causes, and consequences of events that may happen during movement (e.g., harsh brake, sharp turn), it is necessary to analyze event context. The context includes dynamic attributes of the moving objects before and after the event and external context elements such as other moving objects, weather, terrain, etc. To explore events in context, we propose an analytical workflow including event contextualization, context pattern detection, and exploration of the spatio-temporal distribution of the detected patterns. The approach involves clustering of events based on the similarity of their contexts and interactive visual techniques for exploration of the distribution of the clusters in time, geographic space, and multidimensional attribute space. In close collaboration with domain experts, we apply our method to real-world vehicle trajectories with the purpose of identifying and investigating potentially dangerous driving behaviors.
Deep Learning Inverse Multidimensional Projections
(The Eurographics Association, 2019) Espadoto, Mateus; Rodrigues, Francisco Caio Maia; Hirata, Nina S. T.; Hirata Jr., Roberto; Telea, Alexandru C.; Landesberger, Tatiana von and Turkay, Cagatay
We present a new method for computing inverse projections from 2D spaces to arbitrary high-dimensional spaces. Given any projection technique, we train a deep neural network to learn a low-to-high dimensional mapping based on a projected training set, and next use this mapping to infer the mapping on arbitrary points. We compare our method with two recent inverse projection techniques on three datasets, and show that our method has similar or higher accuracy, is one to two orders of magnitude faster, and delivers result that match well known ground-truth information about the respective high-dimensional data. Visual analytics Unsupervised learning Dimensionality reduction and manifold learning.
EuroVa 2019: Frontmatter
(The Eurographics Association, 2019) Landesberger, Tatiana; Turkay, Cagatay; Landesberger, Tatiana von and Turkay, Cagatay
Interactive Pattern Analysis of Multiple T-Maze Data
(The Eurographics Association, 2019) Bechtold, Fabrizia; Abraham, Hrvoje; Splechtna, Rainer; Matkovic, Krešimir; Landesberger, Tatiana von and Turkay, Cagatay
The Multiple T-Maze study is one of the standard methods used in ethology and behaviourism. In this paper we extend the current state of the art in analysis of Multiple T-Maze data for animal cohorts. We focus on pattern finding within animals' paths. We introduce the Sequence View which makes it possible to quickly spot patterns and to search for specific sub-paths in animal paths. Further, we also evaluate four different metrics for string comparison and two widely used embeddings to support interactive clustering. All views are fully integrated in a coordinated multiple views system and support active brushing. This research represents a step towards (semi)-automatic clustering for Multiple T-Maze cohort data, which will significantly improve the Multiple T-Maze data analysis.
Interactive Visual Analysis of Patient-Reported Outcomes for Improved Cancer Aftercare
(The Eurographics Association, 2019) Müller, Juliane; Zebralla, Veit; Wiegand, Susanne; Oeltze-Jafra, Steffen; Landesberger, Tatiana von and Turkay, Cagatay
The monitoring and planning of cancer aftercare are commonly based on clinical, physiological and caregiver-reported outcome measures. More recently, patient-reported outcome (PRO) measures, capturing social, psychological, and financial aspects, are gaining attention in the course of establishing a patient-centered healthcare system. PROs are acquired during regular aftercare consultations where patients are asked to fill in questionnaires. We present an interactive visual analysis (IVA) approach to investigating PROs. The approach is applied in clinical routine during the aftercare consultation to assess the development of the particular patient, to compare this development to those of similar patients, and to detect trends that may require an adaptation of the aftercare strategy. Furthermore, the approach is employed in clinical research to identify groups of similarly developing patients and risk factors for poor outcomes, as well as to visually compare patient groups. We demonstrate the IVA approach in analyzing PROs of 1025 head and neck cancer patients. In an evaluation with 20 clinicians, we assessed the usefulness and usability of a prototypical implementation.
Moving Together: Towards a Formalization of Collective Movement
(The Eurographics Association, 2019) Buchmüller, Juri; Cakmak, Eren; Andrienko, Natalia; Andrienko, Gennady; Jolles, Jolle W.; Keim, Daniel A.; Landesberger, Tatiana von and Turkay, Cagatay
While conventional applications for spatiotemporal datasets mostly focus on the relation between movers and environment, research questions in the analysis of collective movement typically focus more on relationships and dynamics between the moving entities themselves. Instead of concentrating on origin, destination and the way in between, this inter-mover perspective on spatiotemporal data allows to explain how moving groups are coordinating. Yet, only few visualization and Visual Analytics approaches focus on the relationships between movers. To illuminate this research gap, we propose initial steps towards a comprehensive formalization of coordination in collective movement based on temporal autocorrelation of distance matrices derived from basic movement characteristics. We exemplify how patterns can be encoded using autocorrelation cubes and outline the next steps towards an exhaustive formalization of coordination patterns.
On Quality Indicators for Progressive Visual Analytics
(The Eurographics Association, 2019) Angelini, Marco; May, Thorsten; Santucci, Giuseppe; Schulz, Hans-Jörg; Landesberger, Tatiana von and Turkay, Cagatay
A key component in using Progressive Visual Analytics (PVA) is to be able to gauge the quality of intermediate analysis outcomes. This is necessary in order to decide whether a current partial outcome is already good enough to cut a long-running computation short and to proceed. To aid in this process, we propose ten fundamental quality indicators that can be computed and displayed to gain a better understanding of the progress of the progression and of the stability and certainty of an intermediate outcome. We further highlight the use of these fundamental indicators to derive other quality indicators, and we show how to apply the indicators in two use cases.
Quantifying Uncertainty in Multivariate Time Series Pre-Processing
(The Eurographics Association, 2019) Bors, Christian; Bernard, Jürgen; Bögl, Markus; Gschwandtner, Theresia; Kohlhammer, Jörn; Miksch, Silvia; Landesberger, Tatiana von and Turkay, Cagatay
In multivariate time series analysis, pre-processing is integral for enabling analysis, but inevitably introduces uncertainty into the data. Enabling the assessment of the uncertainty and allowing uncertainty-aware analysis, the uncertainty needs to be quantified initially. We address this challenge by formalizing the quantification of uncertainty for multivariate time series preprocessing. To tackle the large design space, we elaborate key considerations for quantifying and aggregating uncertainty. We provide an example how the quantified uncertainty is used in a multivariate time series pre-processing application to assess the effectiveness of pre-processing steps and adjust the pipeline to minimize the introduction of uncertainty.
SurviVIS: Visual Analytics for Interactive Survival Analysis
(The Eurographics Association, 2019) Corvò, Alberto; Garcia Caballero, Humberto; Westenberg, Michel; Landesberger, Tatiana von and Turkay, Cagatay
The increasing quantity of data in biomedical informatics is leading towards better patient profiling and personalized medicine. Lab tests, medical images, and clinical data represent extraordinary sources for patient characterization. While retrospective studies focus on finding correlations in this sheer volume of data, potential new biomarkers are difficult to identify. A common approach is to observe patient mortality with respect to different clinical variables in what is called survival analysis. Kaplan-Meier plots, also known as survival curves, are generally used to examine patient survival in retrospective and prognostic studies. The plot is very intuitive and hence very popular in the medical domain to disclose evidence of poor or good prognosis. However, the Kaplan-Meier plots are mostly static and the data exploration of the plotted cohorts can be performed only with additional analysis. There is a need to make survival plots interactive and to integrate potential prognostic data that may reveal correlations with disease progression. We introduce SurviVIS, a visual analytics approach for interactive survival analysis and data integration on Kaplan-Meier plots. We demonstrate our work on a melanoma dataset and in the perspective of a potential use case in precision imaging.
TourDino: A Support View for Confirming Patterns in Tabular Data
(The Eurographics Association, 2019) Eckelt, Klaus; Adelberger, Patrick; Zichner, Thomas; Wernitznig, Andreas; Streit, Marc; Landesberger, Tatiana von and Turkay, Cagatay
Seeking relationships and patterns in tabular data is a common data exploration task. To confirm hypotheses that are based on visual patterns observed during exploratory data analysis, users need to be able to quickly compare data subsets, and get further information on the significance of the result and the statistical test applied. Existing tools, however, either focus on the comparison of a single data type, such as comparing numerical attributes only, or provide little or no statistical evaluation to assess a hypothesis. To fill this gap, we present TourDino, a support view that helps users who are not experts in statistics to verify generated hypotheses and confirm insights gained during the exploration of tabular data. In TourDino we present an overview of the statistical significance of various row or column comparisons. On demand, we show further details, including the test score, a textual description, and a detail visualization explaining the results. To demonstrate the efficacy of our approach, we have integrated TourDino in the Ordino drug discovery platform for the purpose of identifying new drug targets.
Visual Analysis of Degree-of-Interest Functions to Support Selection Strategies for Instance Labeling
(The Eurographics Association, 2019) Bernard, Jürgen; Hutter, Marco; Ritter, Christian; Lehmann, Markus; Sedlmair, Michael; Zeppelzauer, Matthias; Landesberger, Tatiana von and Turkay, Cagatay
Manually labeling data sets is a time-consuming and expensive task that can be accelerated by interactive machine learning and visual analytics approaches. At the core of these approaches are strategies for the selection of candidate instances to label. We introduce degree-of-interest (DOI) functions as atomic building blocks to formalize candidate selection strategies. We introduce a taxonomy of DOI functions and an approach for the visual analysis of DOI functions, which provide novel complementary views on labeling strategies and DOIs, support their in-depth analysis and facilitate their interpretation. Our method shall support the generation of novel and better explanation of existing labeling strategies in future.
Visual Analytics of Conversational Dynamics
(The Eurographics Association, 2019) Seebacher, Daniel; Fischer, Maximilian T.; Sevastjanova, Rita; Keim, Daniel A.; El-Assady, Mennatallah; Landesberger, Tatiana von and Turkay, Cagatay
Large-scale interaction networks of human communication are often modeled as complex graph structures, obscuring temporal patterns within individual conversations. To facilitate the understanding of such conversational dynamics, episodes with low or high communication activity as well as breaks in communication need to be detected to enable the identification of temporal interaction patterns. Traditional episode detection approaches are highly dependent on the choice of parameters, such as window-size or binning-resolution. In this paper, we present a novel technique for the identification of relevant episodes in bi-directional interaction sequences from abstract communication networks. We model communication as a continuous density function, allowing for a more robust segmentation into individual episodes and estimation of communication volume. Additionally, we define a tailored feature set to characterize conversational dynamics and enable a user-steered classification of communication behavior. We apply our technique to a real-world corpus of email data from a large European research institution. The results show that our technique allows users to effectively define, identify, and analyze relevant communication episodes.
Visual Analytics of Event Data using Multiple Mining Methods
(The Eurographics Association, 2019) Adnan, Muhammad; Nguyen, Phong; Ruddle, Roy; Turkay, Cagatay; Landesberger, Tatiana von and Turkay, Cagatay
Most researchers use a single method of mining to analyze event data. This paper uses case studies from two very different domains (electronic health records and cybersecurity) to investigate how researchers can gain breakthrough insights by combining multiple event mining methods in a visual analytics workflow. The aim of the health case study was to identify patterns of missing values, which was daunting because the 615 million missing values occurred in 43,219 combinations of fields. However, a workflow that involved exclusive set intersections (ESI), frequent itemset mining (FIM) and then two more ESI steps allowed us to identify that 82% of the missing values were from just 244 combinations. The cybersecurity case study's aim was to understand users' behavior from logs that contained 300 types of action, gathered from 15,000 sessions and 1,400 users. Sequential frequent pattern mining (SFPM) and ESI highlighted some patterns in common, and others that were not. For the latter, SFPM stood out for its ability to action sequences that were buried within otherwise different sessions, and ESI detected subtle signals that were missed by SFPM. In summary, this paper demonstrates the importance of using multiple perspectives, complementary set mining methods and a diverse workflow when using visual analytics to analyze complex event data.
Visualization of Rubik's Cube Solution Algorithms
(The Eurographics Association, 2019) Steinparz, Christian Alexander; Hinterreiter, Andreas; Stitz, Holger; Streit, Marc; Landesberger, Tatiana von and Turkay, Cagatay
Rubik's Cube is among the world's most famous puzzle toys. Despite its relatively simple principle, it requires dedicated, carefully planned algorithms to be solved. In this paper, we present an approach to visualize how different solution algorithms navigate through the high-dimensional space of Rubik's Cube states. We use t-distributed stochastic neighbor embedding (t-SNE) to project feature vector representations of cube states to two dimensions. t-SNE preserves the similarity of cube states and leads to clusters of intermediate states and bundles of cube solution pathways in the projection. Our prototype implementation allows interactive exploration of differences between algorithms, showing detailed state information on demand.
Visualizing Event Sequences as Oscillating Streams
(The Eurographics Association, 2019) Weaver, Chris; Etemadpour, Ronak; Landesberger, Tatiana von and Turkay, Cagatay
In this paper, we introduce a new method to visually represent sequence structure in data. Like other methods for visualizing temporal or ordinal data, the representation directly maps absolute time or relative ordering of events from left to right horizontally. Unlike other methods, it also accumulates subsequences of events into streams that oscillate up and down vertically. By interactively adjusting the number of steps between vertical reversals, one can rapidly switch perspectives to show variation in event densities over time (one step), overall patterns of event accumulation (all steps), or short-range patterns of event accumulation (in between). In between, the representation reverses stream direction every N steps, accentuating variations in event accumulation while at the same time preserving visual continuity. We present a user study that compares the stream representation to Dotplots. The study validates the readability of the representation for effective visualization of sequence information in text data. It also shows how pairing stream and Dotplot views outperforms both of them individually for some analysis tasks.
Visually Analyzing Latent Accessibility Clusters of Urban POIs
(The Eurographics Association, 2019) Kamw, Farah; AL-Dohuki, Shamal; Zhao, Ye; Yang, Jing; Ye, Xinyue; Chen, Wei; Landesberger, Tatiana von and Turkay, Cagatay
Accessibility of urban POIs (Points of Interest) is a key topic in a variety of urban sciences and applications as it reflects inherent city design, transportation, and population flow features. Isochrone maps and other techniques have been used to identify and display reachable regions from given POIs. In addition, domain experts further want to study the distribution effects of accessibility in the urban space such as finding spatial regions that have different accessibility patterns. Such patterns can be manifested by clustering POIs based on their accessibility of different time periods under different traffic conditions. In this paper, we present a visualization system that helps users to find and visualize Latent Accessibility Clusters (LACs) of POIs. The LACs discover temporally changing urban sub-regions (including nearby POIs) with disparate accessibilities at different times. LACs are computed by a POIGraph which connects POIs into a graph structure by extending the dual road network of the corresponding city. The LAC computation is facilitated by graph traversal over the POIGraph. By visualizing the LAC regions on the map, users can visually study the hidden patterns of spatial accessibility. It can contribute to urban transportation, planning, business, and related social sciences.

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