@inproceedings{10.2312:eurova.20151095,

booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {E. Bertini and J. C. Roberts
},
title = {{Supporting Historical Research Through User-Centered Visual Analytics}},

author = {Boukhelifa, Nadia and 
Giannisakis, Emmanouil and 
Dimara, Evanthia and 
Willett, Wesley and 
Fekete, Jean-Daniel
},
year = {2015},

publisher = {The Eurographics Association},


DOI = {10.2312/eurova.20151095}

}

@inproceedings{10.2312:eurova.20151096,

booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {E. Bertini and J. C. Roberts
},
title = {{Inspector Gadget: Integrating Data Preprocessing and Orchestration in the Visual Analysis Loop}},

author = {Krüger, Robert and 
Herr, Dominik and 
Haag, Florian and 
Ertl, Thomas
},
year = {2015},

publisher = {The Eurographics Association},


DOI = {10.2312/eurova.20151096}

}

@inproceedings{10.2312:eurova.20151098,

booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {E. Bertini and J. C. Roberts
},
title = {{Interactive Image Feature Selection Aided by Dimensionality Reduction}},

author = {Rauber, Paulo Eduardo and 
Silva, Renato Rodrigues Oliveira da and 
Feringa, Sander and 
Celebi, M. Emre and 
Falcão, Alexandre X. and 
Telea, Alexandru C.
},
year = {2015},

publisher = {The Eurographics Association},


DOI = {10.2312/eurova.20151098}

}

@inproceedings{10.2312:eurova.20151097,

booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {E. Bertini and J. C. Roberts
},
title = {{Visual Scaffolding in Integrated Spatial and Nonspatial Analysis}},

author = {Marai, G. Elisabeta
},
year = {2015},

publisher = {The Eurographics Association},


DOI = {10.2312/eurova.20151097}

}

@inproceedings{10.2312:eurova.20151099,

booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {E. Bertini and J. C. Roberts
},
title = {{The Use of High-Dimensional Visualizations in Explaining Hospital Pricing Patterns}},

author = {Perkins, Miriam and 
Grinstein, Georges
},
year = {2015},

publisher = {The Eurographics Association},


DOI = {10.2312/eurova.20151099}

}

@inproceedings{10.2312:eurova.20151100,

booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {E. Bertini and J. C. Roberts
},
title = {{Attribute-based Visual Explanation of Multidimensional Projections}},

author = {Silva, Renato R. O. da and 
Rauber, Paulo E. and 
Martins, Rafael M. and 
Minghim, Rosane and 
Telea, Alexandru C.
},
year = {2015},

publisher = {The Eurographics Association},


DOI = {10.2312/eurova.20151100}

}

@inproceedings{10.2312:eurova.20151102,

booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {E. Bertini and J. C. Roberts
},
title = {{Trajectory-based Visual Analytics for Anomalous Human Movement Analysis using Social Media}},

author = {Chae, Junghoon and 
Cui, Yuchen and 
Jang, Yun and 
Wang, Guizhen and 
Malik, Abish and 
Ebert, David S.
},
year = {2015},

publisher = {The Eurographics Association},


DOI = {10.2312/eurova.20151102}

}

@inproceedings{10.2312:eurova.20151103,

booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {E. Bertini and J. C. Roberts
},
title = {{Integrated Spatial Uncertainty Visualization using Off-screen Aggregation}},

author = {Jäckle, Dominik and 
Senaratne, Hansi and 
Buchmüller, Juri and 
Keim, Daniel A.
},
year = {2015},

publisher = {The Eurographics Association},


DOI = {10.2312/eurova.20151103}

}

@inproceedings{10.2312:eurova.20151101,

booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {E. Bertini and J. C. Roberts
},
title = {{Visual-Interactive Text Analysis to Support Political Decision Making - From Sentiments to Arguments to Policies}},

author = {Ruppert, Tobias and 
Bernard, Jürgen and 
Lücke-Tieke, Hendrik and 
May, Thorsten and 
Kohlhammer, Jörn
},
year = {2015},

publisher = {The Eurographics Association},


DOI = {10.2312/eurova.20151101}

}

@inproceedings{10.2312:eurova.20151104,

booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {E. Bertini and J. C. Roberts
},
title = {{Visual Analytics and Uncertainty: Its Not About the Data}},

author = {MacEachren, Alan M.
},
year = {2015},

publisher = {The Eurographics Association},


DOI = {10.2312/eurova.20151104}

}

@inproceedings{10.2312:eurova.20151105,

booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {E. Bertini and J. C. Roberts
},
title = {{Visual Analysis of Relations in Attributed Time-Series Data}},

author = {Steiger, Martin and 
Bernard, Jürgen and 
Schader, Philipp and 
Kohlhammer, Jörn
},
year = {2015},

publisher = {The Eurographics Association},


DOI = {10.2312/eurova.20151105}

}

@inproceedings{10.2312:eurova.20151106,

booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {E. Bertini and J. C. Roberts
},
title = {{Exploration and Assessment of Event Data}},

author = {Bodesinsky, Peter and 
Alsallakh, Bilal and 
Gschwandtner, Theresia and 
Miksch, Silvia
},
year = {2015},

publisher = {The Eurographics Association},


DOI = {10.2312/eurova.20151106}

}

@inproceedings{10.2312:eurova.20151107,

booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {E. Bertini and J. C. Roberts
},
title = {{Integrating Predictions in Time Series Model Selection}},

author = {Bögl, Markus and 
Aigner, Wolfgang and 
Filzmoser, Peter and 
Gschwandtner, Theresia and 
Lammarsch, Tim and 
Miksch, Silvia and 
Rind, Alexander
},
year = {2015},

publisher = {The Eurographics Association},


DOI = {10.2312/eurova.20151107}

}

@inproceedings{10.2312:eurova.20151108,

booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {E. Bertini and J. C. Roberts
},
title = {{Gnaeus: Utilizing Clinical Guidelines for Knowledge-assisted Visualisation of EHR Cohorts}},

author = {Federico, Paolo and 
Unger, Jürgen and 
Amor-Amorós, Albert and 
Sacchi, Lucia and 
Klimov, Denis and 
Miksch, Silvia
},
year = {2015},

publisher = {The Eurographics Association},


DOI = {10.2312/eurova.20151108}

}

Browse

Now showing 1 - 15 of 15

Frontmatter: EuroVA 2015 EuroVis Workshop on Visual Analytics
(Eurographics Association, 2015) Roberts, Jonathan C.; Bertini, Enrico; -
Supporting Historical Research Through User-Centered Visual Analytics
(The Eurographics Association, 2015) Boukhelifa, Nadia; Giannisakis, Emmanouil; Dimara, Evanthia; Willett, Wesley; Fekete, Jean-Daniel; E. Bertini and J. C. Roberts
In this paper we describe the development and evaluation of a visual analytics tool to support historical research. Historians continuously gather data related to their scholarly research from archival visits and background search. Organising and making sense of all this data can be challenging as many historians continue to rely on analog or basic digital tools. We built an integrated note-taking environment for historians which unifies a set of functionalities we identified as important for historical research including editing, tagging, searching, sharing and visualization. Our approach was to involve users from the initial stage of brainstorming and requirement analysis through to design, implementation and evaluation. We report on the process and results of our work, and conclude by reflecting on our own experience in conducting user-centered visual analytics design for digital humanities.
Inspector Gadget: Integrating Data Preprocessing and Orchestration in the Visual Analysis Loop
(The Eurographics Association, 2015) Krüger, Robert; Herr, Dominik; Haag, Florian; Ertl, Thomas; E. Bertini and J. C. Roberts
Nowadays, tracking devices are small and cheap. For analysis tasks, there is no problem to obtain sufficient amounts of data. The challenge is how to make sense of the data, which often contain complex situations. Multiple data sources related to time, space, and other dimensions, with different resolution and notation have to be mapped. Visual approaches often cover an analysis loop that starts right after the preprocessing. In this paper, we contribute methods to explicitly integrate data preprocessing and orchestration into the visual analysis loop. Subsequently, the big picture can be explored in detail and hypotheses can be created, refined, and validated. We showcase our approach with multiple heterogeneous datasets from the VAST Challenge 2014.
Interactive Image Feature Selection Aided by Dimensionality Reduction
(The Eurographics Association, 2015) Rauber, Paulo Eduardo; Silva, Renato Rodrigues Oliveira da; Feringa, Sander; Celebi, M. Emre; Falcão, Alexandre X.; Telea, Alexandru C.; E. Bertini and J. C. Roberts
Feature selection is an important step in designing image classification systems. While many automatic feature selection methods exist, most of them are opaque to their users. We consider that users should be able to gain insight into how observations behave in the feature space, since this may allow the design of better features and the incorporation of domain knowledge. For this purpose, we propose a methodology for interactive and iterative selection of image features aided by dimensionality reduction plots and complementary exploration tools. We evaluate our proposal on the problem of feature selection for skin lesion image classification.
Visual Scaffolding in Integrated Spatial and Nonspatial Analysis
(The Eurographics Association, 2015) Marai, G. Elisabeta; E. Bertini and J. C. Roberts
Collaborative visual analytics that feature mixtures of spatial and nonspatial data occur across disciplines, and are particularly common in bioinformatics, neuroscience and geospatial analysis. In this work we analyze, from a human-centric perspective, data collected from the design and evaluation of three successful visual analysis tools, spanning seven case studies. We focus on the importance of the users' background to the design process, and we discuss the importance of visual scaffolding to such collaborative, integrated spatial and nonspatial visual analysis tools. Scaffolding is a psychology concept which denotes the support given during a learning process. We further present evidence that spatial and nonspatial coordinated views can serve as a form of visual scaffolding for expert-level, collaborative visual analyses.
The Use of High-Dimensional Visualizations in Explaining Hospital Pricing Patterns
(The Eurographics Association, 2015) Perkins, Miriam; Grinstein, Georges; E. Bertini and J. C. Roberts
The Centers for Medicare and Medicaid Services (CMS) has made public a data set showing what hospitals charged and what Medicare paid for the one hundred most common inpatient stays. By law payments approximate a hospital's cost of providing a service. However, the data shows a wide dollar gap between Medicare payments and what hospitals actually charged. We explore the origins of hospital pricing using the technique of Independent Component Analysis, a form of blind source separation. Discovered source signals were interpreted by putting them into context with conditions, including characteristics of individual hospitals and the marketplaces in which they operate. This high-dimensional data consisting of over 100 variables was explored using Weave, a web-based analysis and visualization environment. Four underlying processes that exert influence on hospital pricing were identified, including one that revealed distinguishing features of hospitals at the extreme high and low ends of the charge distribution. Perhaps surprisingly, hospitals that lie on opposite ends of the price scale were found to have many attributes in common as well.
Attribute-based Visual Explanation of Multidimensional Projections
(The Eurographics Association, 2015) Silva, Renato R. O. da; Rauber, Paulo E.; Martins, Rafael M.; Minghim, Rosane; Telea, Alexandru C.; E. Bertini and J. C. Roberts
Multidimensional projections (MPs) are key tools for the analysis of multidimensional data. MPs reduce data dimensionality while keeping the original distance structure in the low-dimensional output space, typically shown by a 2D scatterplot. While MP techniques grow more precise and scalable, they still do not show how the original dimensions (attributes) influence the projection's layout. In other words, MPs show which points are similar, but not why. We propose a visual approach to describe which dimensions contribute mostly to similarity relationships over the projection, thus explain the projection's layout. For this, we rank dimensions by increasing variance over each point-neighborhood, and propose a visual encoding to show the least-varying dimensions over each neighborhood. We demonstrate our technique with both synthetic and real-world datasets.
Trajectory-based Visual Analytics for Anomalous Human Movement Analysis using Social Media
(The Eurographics Association, 2015) Chae, Junghoon; Cui, Yuchen; Jang, Yun; Wang, Guizhen; Malik, Abish; Ebert, David S.; E. Bertini and J. C. Roberts
The rapid development and increasing availability of mobile communication and location acquisition technologies allow people to add location data to existing social networks so that people share location-embedded information. For human movement analysis, such location-based social networks have been gaining attention as promising data sources. Researchers have mainly focused on finding daily activity patterns and detecting outliers. However, during crisis events, since the movement patterns are irregular, a new approach is required to analyze the movements. To address these challenges, we propose a trajectory-based visual analytics system for analyzing anomalous human movements during disasters using social media. We extract trajectories from location-based social networks and cluster the trajectories into sets of similar sub-trajectories in order to discover common human movement patterns. We also propose a classification model based on historical data for detecting abnormal movements using human expert interaction.
Integrated Spatial Uncertainty Visualization using Off-screen Aggregation
(The Eurographics Association, 2015) Jäckle, Dominik; Senaratne, Hansi; Buchmüller, Juri; Keim, Daniel A.; E. Bertini and J. C. Roberts
Visualization of spatial data uncertainties is crucial to the data understanding and exploration process. Scientific measurements, numerical simulations, and user generated content are error prone sources that gravely influence data reliability. When exploring large spatial datasets, we face two main challenges: data and uncertainty are two different sets which need to be integrated into one visualization, and we often lose the contextual overview when zooming or filtering to see details. In this paper, we present an extrinsic uncertainty visualization as well as an off-screen technique which integrates the uncertainty representation and enables the user to perceive data context and topology in the analysis process. We show the applicability and usefulness of our approach in a use case.
Visual-Interactive Text Analysis to Support Political Decision Making - From Sentiments to Arguments to Policies
(The Eurographics Association, 2015) Ruppert, Tobias; Bernard, Jürgen; Lücke-Tieke, Hendrik; May, Thorsten; Kohlhammer, Jörn; E. Bertini and J. C. Roberts
Political decision making involves the evaluation of alternative solutions (so called policy models) to a given societal problem and the selection of the most promising one. Large amounts of textual information to be considered in decision making processes can be found on the web. This includes general information about policy models, individual arguments in favor or against these policies, and public opinions. Monitoring large text collections and extracting the relevant information is time consuming. In this approach we present a visual analytics system that supports users in assessing the results of automatic text analysis methods. The methods extract text segments from large document collections and associate them with predefined policy domains, policy models, and policy arguments. Moreover, sentiment analysis is applied on the text segments. Visualization techniques provide non-IT experts an intuitive access to the results. With the system, users can monitor public debates on the web. In addition, we analyze concepts that enable the user to give visual-interactive feedback on the text analysis results. This direct user feedback can help to improve the accuracy of individual text analysis modules and the credibility of the overall text analysis process. The system was tested with real users from the political decision making domain.
Visual Analytics and Uncertainty: Its Not About the Data
(The Eurographics Association, 2015) MacEachren, Alan M.; E. Bertini and J. C. Roberts
Uncertainty visualization research has a long history, with contributions from scientific, information, geo-graphic and other visualization perspectives as well as from cognitive and HCI perspectives. But we still do not have generally accepted strategies for leveraging visualization to cope with uncertainty. Here, I argue that taking a visual analytics rather than visualization perspective can overcome this inertia. While uncertainty visualization research has focused on visually signifying and interacting with data uncertainty, taking a visual analytics approach recognizes that the challenge is about much more than uncertain data. The larger challenge is to enable reasoning under uncertainty (in all its forms). In this short paper, I sketch elements of what we know and outline some key challenges for developing visual analytics methods and tools that enable users to cope with uncertainty throughout the processes of sensemaking, decision-making, and action-taking.
Visual Analysis of Relations in Attributed Time-Series Data
(The Eurographics Association, 2015) Steiger, Martin; Bernard, Jürgen; Schader, Philipp; Kohlhammer, Jörn; E. Bertini and J. C. Roberts
In this paper, we present visual-interactive techniques for revealing relations between two co-existing multivariate feature spaces. Such data is generated, for example, by sensor networks characterized by a set of (categorical) attributes which continuously measure physical quantities over time. A challenging analysis task is the seeking for interesting relations between the time-oriented data and the sensor attributes. Our approach uses visualinteractive analysis to enable analysts to identify correlations between similar time series and similar attributes of the data. It is based on a combination of machine-based encoding of this information in position and color and the human ability to recognize cohesive structures and patterns. In our figures, we illustrate how analysts can identify similarities and anomalies between time series and categorical attributes of metering devices and sensors.
Exploration and Assessment of Event Data
(The Eurographics Association, 2015) Bodesinsky, Peter; Alsallakh, Bilal; Gschwandtner, Theresia; Miksch, Silvia; E. Bertini and J. C. Roberts
Event data is generated in many domains, like business process management, industry or healthcare. These datasets are often unstructured, exhibit variant behaviour, and may contain errors. Before applying automated analysis methods, such as process mining algorithms, the analyst needs to understand the dependency between events in order to decide which analysis method might fit the recorded events. We define a categorization scheme of event dependencies and describe a preliminary approach for exploring event data, combining visual exploration with pattern mining. Events of interest can be selected, grouped, and visually explored, using either a sequential or a temporal scale. We present two use cases with shopping event data and report expert feedback on our approach.
Integrating Predictions in Time Series Model Selection
(The Eurographics Association, 2015) Bögl, Markus; Aigner, Wolfgang; Filzmoser, Peter; Gschwandtner, Theresia; Lammarsch, Tim; Miksch, Silvia; Rind, Alexander; E. Bertini and J. C. Roberts
Time series appear in many different domains. The main goal in time series analysis is to find a model for given time series. The selection of time series models is done iteratively based, usually, on information criteria and residual plots. These sources may show only small variations and, therefore, it is necessary to consider the prediction capabilities in the model selection process. When applying the model and including the prediction in an interactive visual interface it is still difficult to compare deviations from actual values or benchmark models. Judging which model fits the time series adequately is not well supported in current methods. We propose to combine visual and analytical methods to integrate the prediction capabilities in the model selection process and assist in the decision for an adequate and parsimonious model. In our approach a visual interactive interface is used to select and adjust time series models, utilize the prediction capabilities of models, and compare the prediction of multiple models in relation to the actual values.
Gnaeus: Utilizing Clinical Guidelines for Knowledge-assisted Visualisation of EHR Cohorts
(The Eurographics Association, 2015) Federico, Paolo; Unger, Jürgen; Amor-Amorós, Albert; Sacchi, Lucia; Klimov, Denis; Miksch, Silvia; E. Bertini and J. C. Roberts
The advanced visualization of electronic health records (EHRs), supporting a scalable analysis from single patients to cohorts, intertwining patients' conditions with executed treatments, and handling the complexity of timeoriented data, is an open challenge of visual analytics for health care. We propose an approach that, according to the knowledge-assisted visualization paradigm, leverages the domain knowledge acquired by clinical experts and formalized into computer-interpretable guidelines (CIGs), in order to improve the automated analysis, the visualization, and the interactive exploration of EHRs of patient cohorts. In this way, the analyst can get insights about the clinical history of multiple patients and assess the effectiveness of their health care treatments.

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