EuroVA: International Workshop on Visual Analytics

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https://diglib.eg.org/handle/10.2312/982

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Capturing Reasoning Process through User Interaction
(The Eurographics Association, 2010) Dou, Wenwen; Ribarsky, William; Chang, Remco; Joern Kohlhammer and Daniel Keim
In recent years, visual analytics has taken an important role in solving many types of complex analytical problems that require deep and specific domain knowledge from users. While the analysis products generated by these expert users are of great importance, how these users apply their domain expertise in using the visualization to validate their hypotheses and arrive at conclusions is often just as invaluable. Recent research efforts in capturing an expert's reasoning process using a visualization have shown that some of a user's analysis process is indeed recoverable. However, there does not exist a generalizable principle that explains the success of these domainspecific systems in capturing the user's reasoning process. In this paper, we present a framework that examines two aspects of the capturing process. First, we inspect how a user's reasoning process can be captured by utilizing van Wijk's operational model of visualization. Second, we evaluate the likelihood of success in capturing a user's interactions in a visualization by introducing three criteria designed for disambiguating the meanings behind the interactions. Various visualization systems in the visualization and HCI communities are examined for the purpose of demonstrating the impact of the three criteria.
Does Jason Bourne need Visual Analytics to catch the Jackal?
(The Eurographics Association, 2010) Bertone, Alessio; Lammarsch, Tim; Turic, Thomas; Aigner, Wolfgang; Miksch, Silvia; Joern Kohlhammer and Daniel Keim
Visual Analytics is a relatively new field which tries to combine and intertwine visual and analytical methods in an interactive manner. Because of the complex structure of time, the application of visual analytics methods to time-oriented data is a very promising approach for insight generation. To show how this can be applied, on top of real world data we created a fictitious scenario where even one of Ludlum's heroes, Jason Bourne, could take advantage of the collaboration between visual and analytical methods.
Comparative Visual Analysis of Cross-Linguistic Features
(The Eurographics Association, 2010) Rohrdantz, Christian; Mayer, Thomas; Butt, Miriam; Plank, Frans; Keim, Daniel A.; Joern Kohlhammer and Daniel Keim
Approaches in Visual Analytics have so far been developed for a wide array of research areas, mainly with a focus on industrial or business applications. The field of linguistics, however, has only marginally incorporated visualizations in its research, e.g. using simple tree representations, attribute-value matrices or network analyses. This paper suggests a new interesting field of application demonstrating how Visual Analytics is able to support linguists in their research. We show this with respect to one concrete linguistic phenomenon, named Vowel Harmony, where visual analysis allows an at-a-glance comparison across a variety of languages. Our approach covers the entire pipeline of Visual Analytics methodology: data processing, feature extraction and the creation of an interactive visual representation. Our results allow for a novel approach to linguistic investigation in that we enable an at-a-glance analysis of whether vowel harmony is present in a language and, beyond that, a precise indication of the particular type of vowel interdependence and patterning in a given language.
Finding Arbitrary Shaped Clusters with Related Extents in Space and Time
(The Eurographics Association, 2010) Pölitz, Christian; Andrienko, Gennady; Andrienko, Natalia; Joern Kohlhammer and Daniel Keim
The paper deals with density-based clustering of events, i.e. objects positioned in space and time, such as occurrences of earthquakes, forest fires, mobile phone calls, or photos with geographical references. Finding concentrations of events in space and time can help to discover interesting places and time periods. The spatial and temporal properties of event clusters, in particular, their spatial and temporal extents and densities, can be related to each other. Therefore, we suggest a two-step clustering method that considers relations of the spatial and temporal dimension. We demonstrate the work of the method on several examples of real data.
Sensemaking in Visual Analytics: Processes and Challenges
(The Eurographics Association, 2010) Attfield, Simon J.; Hara, Sukhvinder K.; Wong, B. L. William; Joern Kohlhammer and Daniel Keim
Since Visual Analytic systems support human sensemaking it is essential that such systems are designed with characteristics of this process in mind. Drawing on our previous work with lawyers and reports from experienced fraud investigators we describe the nature of the cognitive work to be supported. We describe the cognitive work domain in terms of its data characteristics, and develop a model of the sensemaking as basis for discussing a distinction between 'naturalistic' and 'normative' sensemaking with a particular emphasis on inference types and the potential for bias. We also report results from a questionnaire-based case study designed to elicit memorable incidents from fraud investigators' experiences. Given the legal context the case study exemplifies skills and strategies that are necessary in order to achieve normative and defensible sensemaking under pressure of highvolume datasets.
Interactive Visual Analysis of Families of Surfaces: An Application to Car Race and Car Setup
(The Eurographics Association, 2010) Matkovic, Kresimir; Gracanin, Denis; Splechtna, Reiner; Hauser, Helwig; Joern Kohlhammer and Daniel Keim
Modern simulations often produce time series, or even functions of two variables as outputs for single attributes. Such complex data require carefully chosen and designed analysis procedures and the corresponding data model. The use of previously developed curve and surface views provides strong support for visual exploration and analysis of complex data. In this paper we describe how interactive visual analysis can support users in getting insight into complex data. The case study, based on TORCS 3D racing cars simulator, illustrates our approach and its successful application to a real world problem. The analysis of the car parameters and driving performances during races provides an insight and explanation for race results. That insight is then used to fine-tune car parameters to achieve better driving performance.
Smart Query Definition for Content-Based Search in Large Sets of Graphs
(The Eurographics Association, 2010) Landesberger, Tatiana von; Bremm, Sebastian; Bernard, Jürgen; Schreck, Tobias; Joern Kohlhammer and Daniel Keim
Graphs are used in various application areas such as chemical, social or shareholder network analysis. Finding relevant graphs in large graph databases is thereby an important problem. Such search starts with the definition of the query object. Defining the query graph quickly and effectively so that it matches meaningful data in the database is difficult. In this paper, we introduce a system, which guides the user through the process of query graph building. We propose three approaches for graph definition. First, query by example selection starting from an overview of the graph types in the database, second query by sketch combining graph building blocks (i.e., topologic subgraphs) with free graph drawing, and third a combination of both approaches. In all three query definition ways, we support the user with intelligent, data dependent recommendations. It covers the whole spectrum of building parameters such as representative examples, frequent building blocks, or common graph size.
Combining Details of the Chi-Square Goodness-of-Fit Test with Multivariate Data Visualization
(The Eurographics Association, 2010) May, Thorsten; Davey, James; Kohlhammer, Jörn; Joern Kohlhammer and Daniel Keim
In this work, we combine KVMaps, a visualization technique presented in [May07] for the visualization of statistical aggregations in multivariate contingency tables, with the measures used for the statistical Chi-Square goodness-of-fit test. Goodness-of-fit tests are used to check whether a given distribution of values matches an expected distribution. A single test statistic is calculated to represent the deviation of the complete dataset. By visualizing the deviations for all entries in the contingency table, it is possible to identify the patterns in the distribution of data items, which contribute most to the overall deviation of the dataset. We present two use cases to illustrate how the information about the patterns can be used.
Stress Maps: Analysing Local Phenomena in Dimensionality Reduction Based Visualisations
(The Eurographics Association, 2010) Seifert, Christin; Sabol, Vedran; Kienreich, Wolfgang; Joern Kohlhammer and Daniel Keim
Challenges in Visual Analytics frequently involve massive repositories, which do not only contain a large number of information artefacts, but also a high number of relevant dimensions per artefact. Dimensionality reduction algorithms are commonly used to transform high-dimensional data into low- dimensional representations which are suitable for visualisation purposes. For example, Information Landscapes visualise high-dimensional data in two dimensions using distance-preserving projection methods. The inaccuracies introduced by such methods are usually expressed through a global stress measure which does not provide insight into localised phenomena. In this paper, we propose the use of Stress Maps, a combination of heat maps and information landscapes, to support algorithm development and optimization based on local stress measures. We report on an application of Stress Maps to a scalable text projection algorithm and describe two categories of problems related to localised stress phenomena which we have identified using the proposed method.
Policy-making in a Complex World: Can Visual Analytics Help?
(The Eurographics Association, 2010) Osimo, David; Lampathaki, Fenareti; Charalabidis, Yannis; Joern Kohlhammer and Daniel Keim
As government are moving their focus from service provision to regulation, socio-economic issue are revealing more complex than ever to regulate. Traditional top-down and linear models for dealing with risks proved uneffective, as the financial crisis has shown. In recent years, new ICT tools have emerged that take better account of complexity and wicked problems, namely by augmenting human intelligence rather than trying to substitute for it: for example, social networking, crowdsourcing, social simulation and visual analytics. Yet these tools are far from being widespread and the related research fields are still fragmented. The CROSSROAD project, co-funded by the European Commission, aims at drawing a common research agenda in the field of ICT for Governance and Policy Modelling, including approaches such as visual analytics, that enable the identification of unexpected risks and the augmenting of human intelligence in dealing with large amounts of data. This paper presents the approach and the first results of the project, with the aim to start a wider discussion with the visual analytics community on future research issues.
Visual Analytics to Check Marine Containers in the Eritr\@c Project
(The Eurographics Association, 2010) Aupetit, Michaël; Allano, Lorène; Espagnon, Isabelle; Sannie, Guillaume; Joern Kohlhammer and Daniel Keim
The Eritr@c project is a European project aiming at developing an inspection system to control marine containers for illicit or dangerous materials. The system is made of a neutron generator, a set of sensors, and an information system to process the data. One of the main parts of the system is an interactive visualization interface whose goal is to help custom officers to decide if a container must be opened for deeper check of its content. In this paper, we present the components of this visual interface and their use for analytic reasoning.
Utilizing Treemaps for Multicriterial Search of 3D Objects
(The Eurographics Association, 2010) Petkos, Georgios; Darlagiannis, Vasilios; Moustakas, Konstantinos; Tzovaras, Dimitrios; Joern Kohlhammer and Daniel Keim
We propose a treemap based interface for presentation of search results according to multiple search criteria. Different colors are used to represent the relevance of each item in the database according to the different search criteria, while at the same time the treemap based representation allows the user to visually identify relevant groups of data, exploiting the hierarchical organization of the items in the database. Items are ranked according to each criterion and an aggregate ranking is computed using the Borda algorithm. Furthermore, appropriate interaction mechanisms are provided in order to assist the user in refining the presentation of the returned items and weigh the contribution of different criteria for retrieving combined search results. The Princeton benchmark 3D object database is used for this study, nevertheless the technique presented is appropriate for any multicriterial search application and in particular in cases where the data is organized hierarchically.
DYNEVI - DYnamic News Entity Visualization
(The Eurographics Association, 2010) Wanner, Franz; Schaefer, Matthias; Leitner-Fischer, Florian; Zintgraf, Fabian; Atkinson, M.; Keim, Daniel A.; Joern Kohlhammer and Daniel Keim
Dynamic news entity visualization shows an implementation of visualizing news entity data to give an overview as well as to display emerging and vanishing news topics. We present a robust and dynamic visualization system with case studies that show its benefits and high functionality.
Visual Analytics in Software Maintenance: Challenges and Opportunities
(The Eurographics Association, 2010) Telea, Alexandru; Ersoy, Ozan; Voinea, Lucian; Joern Kohlhammer and Daniel Keim
Visual analytics (VA) is an emerging science at the crossroads of data and information visualization, graphics, data mining, and knowledge representation, with many successful applications in engineering, business and finance, security, geosciences, and e-governance and health. Tools using visualization, data mining, and data analysis are also prominently present in a different field: software maintenance. However, an integrated VA is relatively new for this field. In this paper, we discuss the specific challenges and particularities of applying VA in software engineering, highlight the added value of a VA approach, as distilled by us from several large-scale software engineering industrial projects.
Towards a Concept how the Structure of Time can Support the Visual Analytics Process
(The Eurographics Association, 2011) Lammarsch, T.; Aigner, W.; Bertone, A.; Miksch, S.; Rind, A.; Silvia Miksch and Giuseppe Santucci
The primary goal of Visual Analytics (VA) is the close intertwinedness of human reasoning and automated methods. An important task for this goal is formulating a description for such a VA process. We propose the design of a VA process description that uses the inherent structure contained in time-oriented data as a way to improve the integration of human reasoning. This structure can, for example, be seen in the calendar aspect of time being composed of smaller granularities, like years and seasons. Domain experts strongly consider this structure in their reasoning, so VA needs to consider it, too.
Steerable Clustering for Visual Analysis of Ecosystems
(The Eurographics Association, 2011) Ahmed, Zafar; Yost, Patrick; McGovern, Amy; Weaver, Chris; Silvia Miksch and Giuseppe Santucci
One of the great challenges in the geosciences is understanding ecological systems in order to predict changes and responses in space and time at scales from local to global. Ecologists are starting to recognize the value of analysis methods that go beyond statistics to include data mining, visual representations, and combinations of these in computational tools. However, the tools in use today rarely provide means to perform the kinds of rich multidimensional interaction that hold promise to greatly expand possibilities for effective visual exploration and analysis. As part of a project to develop a cyberCommons for collaborative ecological forecasting, we are developing ways to integrate highly interactive visual analysis techniques with data mining algorithms. We describe here our work in progress on steering mixed-dimensional KD-KMeans clustering using multiple coordinated views. Contributions include more flexible interactive control over clustering inputs and outputs, greater consistency of cluster membership during interaction, and higher performance by caching cluster results as a function of interactive state. We present our current tool that implements these improvements for visual analysis of Terrestrial ECOsystem (TECO) data collected from FLUXNET towers, with feedback on utility from our ecologist collaborators.
Exploring Complex Mobile Life through Lightweight Visualizations
(The Eurographics Association, 2011) Kim, Tanyoung; Blom, Jan; Stasko, John; Silvia Miksch and Giuseppe Santucci
Smart phones can be used to collect various types of data from a user spanning call and text message logs to GPS coordinates, and to Bluetooth detection. These data are indicative of behavior across various socio-temporalspatial contexts. For the purpose of inspecting the expected and discovering the unexpected from the data, we developed lightweight visualizations with a low cost. The interactive visualizations are customized with each data analysis task and evolved to support emerging diminutive inquiries during the inspection with themselves. By employing this analytic method at the early phase of data-related research projects, the insights can form a ground for subsequent product development activities focusing on engineering, design and market research.
SmartStripes - Looking under the Hood of Feature Subset Selection Methods
(The Eurographics Association, 2011) May, T.; Davey, J.; Ruppert, T.; Silvia Miksch and Giuseppe Santucci
We propose a visualization method for the diagnosis and interactive refinement of automatic techniques for feature subset selection. So-called filter techniques use statistical ranking measures to identify the most useful combination of features for further analysis. Usually a measure is applied to all entities of a data-table. The influence of atypical entities can distort the result, but this distortion may be masked by the statistical aggregation. Clearly, feature and entity subset selection are highly interdependent. Our technique, SmartStripes, intends to make this interdependency visible.
Visual Analysis of Advanced Manufacturing Simulations
(The Eurographics Association, 2011) Wörner, M.; Ertl, T.; Silvia Miksch and Giuseppe Santucci
Today's manufacturing companies are faced with requests for highly customer-specific products that come in numerous variants but small numbers of units per variant. Therefore, their manufacturing processes need to be flexible and versatile. This opens up opportunities for real-time process planning, yet there is insufficient tool support for process planning that is not done days or weeks in advance. As a result, there is little incentive to collect process data on site. We describe a visual analysis tool that uses advanced manufacturing simulation to evaluate and optimize flexible manufacturing processes.
Visual Comparison of Ranked Result Cumulated Gains
(The Eurographics Association, 2011) Ferro, N.; Sabetta, A.; Santucci, G.; Tino, G.; Veltri, F.; Silvia Miksch and Giuseppe Santucci
Ranking is fundamental in Information Retrieval (IR) and several measures have been developed over the years to assess the quality of a ranked result list, such as those based on the idea of computing the cumulative gain up to a given ranked position and taking into account multiple relevance levels. These measures allow for comparing the performances of different Information Retrieval System (IRS), giving credit to their ability to retrieve highly relevant documents and to rank them topmost in the result list. However, while this approach is able to assess the differences among two or more retrieval systems, it does not allow to easily understand and inspect the reasons of good or bad performances. To this end, this paper presents a Visual Analytics (VA) environment that allows for visually exploring the ranked retrieval results, pointing out the search failures and providing useful insights for improving the underlying IRS ranking algorithm.

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