EuroVisPosters2018

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https://diglib.eg.org/handle/10.2312/2632399

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Case Studies of Shareable Personal Map Visualization
(The Eurographics Association, 2018) Ruchikachorn, Puripant; Anna Puig and Renata Raidou
This paper presents two examples of personal data visualizations to be shared among peers. The visualized and shared data were travel destinations in Thailand and daily commutes in Bangkok, Thailand. The former gathered much attention with almost a million visitors within the first week after launch or approximately 2% of internet users in Thailand. Despite minimal data collection, large data samples of the first case study enable various analyses. The easy-to-use interfaces and simple visualizations can be a model of the genre of personal visualization whose main task is to share.
Categorizing Uncertainties in the Process of Segmenting and Labeling Time Series Data
(The Eurographics Association, 2018) Bögl, Markus; Bors, Christian; Gschwandtner, Theresia; Miksch, Silvia; Anna Puig and Renata Raidou
The segmenting and labeling of multivariate time series data is applied in different domains, e.g. activity recognition or sensor states. This involves several steps of (pre-) processing, segmenting, and labeling of time intervals, and visually exploring the results as well as iteratively refining the parameters for all the processing steps. Within these processes different uncertainties are involved and relevant. In this poster we identify and categorize important uncertainties in this problem domain. We discuss challenges for visually communicating these uncertainties throughout the segmenting and labeling process.
CV3: Visual Exploration, Assessment, and Comparison of CVs
(The Eurographics Association, 2018) Filipov, Velitchko Andreev; Federico, Paolo; Miksch, Silvia; Anna Puig and Renata Raidou
Curriculum Vitae (CV) is an established representation of a person's academic and professional history. A typical CV is comprised of multiple sections associated with spatial, temporal, nominal, and ordinal data. Commonly, comparing and assessing CVs is done by viewing them in a side-by-side fashion. This becomes challenging when comparing more than two CVs, because the reader is required to switch attention back and forth, the overview becomes cluttered, and assessing the CVs becomes a nontrivial task. In order to address this challenge, we propose the design and implementation of an interactive exploration environment capable of comparing multiple CVs, visualizing their information in a clear manner, whilst maintaining a clean overview. Our approach offers users a new way to explore, assess, and compare multiple CVs.
An Eye-Tracking Study on Sparklines within Textual Context
(The Eurographics Association, 2018) Ruchikachorn, Puripant; Rattanawicha, Pimmanee; Anna Puig and Renata Raidou
Sparklines are placed in documents but their usability is rarely evaluated in their immediate context of paragraphs of text. We conducted an eye-tracking study to measure readability and understandability of four different conditions: two different sparkline chart types (bar and line charts) and two text languages (native and non-native languages). We found out that most participants out of 296 in total were not distracted by sparklines. Only 3.19% of the average reading time was spent looking at sparklines. There was no correlation between dwell time and data understanding, measured in a post-experiment quiz. The chart types did not have a significant effect on sparkline attention. However, compared with native textual context, sparklines in non-native text were more noticeable. The results of this study can be useful for future sparkline usage consideration.
The Impact of Visualizing Uncertainty on Train Trip Selection
(The Eurographics Association, 2018) Wunderlich, Marcel; Ballweg, Kathrin; Landesberger, Tatiana von; Anna Puig and Renata Raidou
Train trip planning means deciding on one of several travel connections. Possible train delays lead to uncertainties in the schedule connections and may influence the planning decisions. Although several designs for the visualization of the available train trips exist, it is still unclear how these designs and the visualization of delay uncertainty influence the decision making. We let 86 people decide ten times on different train trips using one of four designs, (not) visualizing delay uncertainty and with(out) temporal constraints. The results show that planning decisions depend on whether the design is visual or textual and on the availability of trip uncertainty visualization. In case of a temporal constraint, non-critical train connections are preferred.
Network Analysis for Financial Fraud Detection
(The Eurographics Association, 2018) Leite, Roger A.; Gschwandtner, Theresia; Miksch, Silvia; Gstrein, Erich; Kuntner, Johannes; Anna Puig and Renata Raidou
Security and quality are main concerns for private and public financial institutions. Data mining techniques based on the profiles of customers of a financial institution are commonly used to avoid fraud and financial damage. However, these approaches often are limited to the analysis of individual customers which hinders the detection of fraudulent networks. We propose a Visual Analytics approach for supporting and fine-tuning customers' network analysis, thus, reducing false-negative alarms of frauds.
Supporting Visual Parameter Analysis of Time Series Segmentation with Correlation Calculations
(The Eurographics Association, 2018) Eichner, Christian; Schumann, Heidrun; Tominski, Christian; Anna Puig and Renata Raidou
Parameter analysis can be used to find out how individual parameters influence the output of an algorithm. We aim to support the visual parameter analysis of algorithms for the segmentation of time series. To this end, we automatically search for correlations between parameters and the segmentation outputs. Correlations are not only determined globally, but also locally within parameter subspaces. Calculated correlations are used to visually emphasize parameter and value ranges with high influence on the segmentation. By interactive exploration, the analyst can study the multidimensional parameter space in depth.
Towards Natural Language Empowered Interactive Data Analysis
(The Eurographics Association, 2018) Turkay, Cagatay; Henkin, Rafael; Anna Puig and Renata Raidou
The recent advances in natural language based interaction methodologies offer promising avenues to enhance the interactive processes within the human-machine dialogue of visual analytics. We envisage Multimodal Data Analytics as a novel approach for conducting data analysis that builds on the strengths of visual analytics and natural language as an expressive interaction channel. We investigate the potential enhancements from such a multimodal approach and discusses the preliminary outline for a structured methodology to study the role of natural language in data analytics. Our approach builds on a simple model of human machine dialogue for interactive data analysis which we then propose to instantiate as visual analytics workflows - representations to study and operationalise interactive data analysis routines empowered by natural language interaction.
Validation of Quantitative Measures for Edge Bundling by Comparing with Human Feeling
(The Eurographics Association, 2018) Saga, Ryosuke; Anna Puig and Renata Raidou
This paper describes an analysis of the relationship between human cognition and quantitative measures for a visualization method called edge bundling.Aesthetic rules-based measures, namely, mean edge length difference (MELD), normalized MELD (NMELD), mean occupation area, and edge density distribution (EDD), for evaluating and quantifying the result of edge bundling are proposed. However, comparing these measures with human cognition has not been analyzed. Therefore, a questionnaire survey with approximately 40 respondents was conducted to clarify the relationship between human cognition and these evaluation measures. Results showed that NMELD, MELD, and EDD demonstrate robust and significant correlations with human cognition.
ViMEC: Interactive Application for Micro-Cluster Visualizations
(The Eurographics Association, 2018) Schmidt, Florian; Ehrenfeld, Yannick; Anna Puig and Renata Raidou
Digitalization increases the opportunity to collect vast amounts of data in a large scale manner. In order to handle the information overload, data mining techniques like online clustering are performed. A lot of online clusterers are based on the concept of micro-clusters in order to represent the given data stream. Based on its definition, micro-clusters can be represented as an n-sphere. Online clustering algorithms like BIRCH or DenStream use different strategies for maintaining the micro-clusters in evolving time series, but using the same underlying key concept storing a summarized version of the data stream in their models. We propose ViMEC, an application for multidimensional micro-cluster visualization, giving the user the opportunity to gain understanding of the internal behaviour of the clustering model. For a given time frame, ViMEC gives the user three different types of visualizations presenting different levels of details: Overview, Pair-view and Detail-view. These views combine not only a summary and detail representations for the different dimensions, but also aim to show different relations between dimensions. Preliminary results show, that large data sets with up to 20,000 data points can be visualized within less than 20 seconds.
Visual Analysis of Sentiment and Stance in Social Media Texts
(The Eurographics Association, 2018) Kucher, Kostiantyn; Paradis, Carita; Kerren, Andreas; Anna Puig and Renata Raidou
Despite the growing interest for visualization of sentiments and emotions in textual data, the task of detecting and visualizing various stances is not addressed well by the existing approaches. The challenges associated with this task include development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this poster abstract, we describe the ongoing work on a visual analytics platform, called StanceVis Prime, which is designed for analysis of sentiment and stance in temporal text data from various social media data sources. Our approach consumes documents from several text stream sources, applies sentiment and stance classification, and provides end users with both an overview of the resulting data series and a detailed view for close reading and examination of the classifiers' output. The intended use case scenarios for StanceVis Prime include social media monitoring and research in sociolinguistics.
A Visual Comparison of Hand-Drawn and Machine-Generated Human Metabolic Pathways
(The Eurographics Association, 2018) Wu, Hsiang-Yun; Nöllenburg, Martin; Viola, Ivan; Anna Puig and Renata Raidou
This poster abstract presents a visual comparison between three hand-drawn and one machine-generated human metabolic pathway diagrams. The human metabolic pathways, which describe significant biochemical reactions in the human body, have been increasingly investigated due to the development of analysis processes and are compiled into pathway diagrams to provide an overview of reaction in human body. This complex network includes about 5,000 metabolites and 7,500 reactions, which are hierarchically nested and difficult to visualize. We collect and analyze well-known human metabolic pathway diagrams, and summarize the design choices of these diagrams, respectively. Together with a machine-generated diagram, we can understand the visual complexity of three hand-drawn and one machine-generated diagrams.
Visually Exploring Data Provenance and Quality of Open Data
(The Eurographics Association, 2018) Bors, Christian; Gschwandtner, Theresia; Miksch, Silvia; Anna Puig and Renata Raidou
While open data platforms are increasingly popular among end-users as well as data providers, there is a growing problem with inconsistent update frequencies and lack of quality in datasets. Efforts to monitor data quality are currently limited to checking meta-information and creating revisions to allow manual inspection of former datasets.We employ a Visual Analytics framework for generating and visualizing data provenance from data quality to facilitate data analysis and help users to understand the impact of updates on the data. Data quality metrics are utilized to quantify the development of data quality over time for open data projects. We combine quality metrics, data provenance, and data transformation information in an interactive exploration environment to expedite assessment and selection of appropriate open datasets.

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