2018, Diehl, Alexandra, Hundt, Michael, Häußler, Johannes, Seebacher, Daniel, Chen, Siming, Cilasun, Nida, Keim, Daniel A., Schreck, Tobias

Social media allows citizens, corporations, and authorities to create, post, and exchange information. The study of its dynamics will enable analysts to understand user activities and social group characteristics such as connectedness, geospatial distribution, and temporal behavior. In this context, social media bubbles can be defined as social groups that exhibit certain biases in social media. These biases strongly depend on the dimensions selected in the analysis, for example, topic affinity, credibility, sentiment, and geographic distribution. In this paper, we present SocialOcean, a visual analytics system that allows for the investigation of social media bubbles. There exists a large body of research in social sciences which identifies important dimensions of social media bubbles (SMBs). While such dimensions have been studied separately, and also some of them in combination, it is still an open question which dimensions play the most important role in defining SMBs. Since the concept of SMBs is fairly recent, there are many unknowns regarding their characterization. We investigate the thematic and spatiotemporal characteristics of SMBs and present a visual analytics system to address questions such as: What are the most important dimensions that characterize SMBs? and How SMBs embody in the presence of specific events that resonate with them? We illustrate our approach using three different real scenarios related to the single event of Boston Marathon Bombing, and political news about Global Warming. We perform an expert evaluation, analyze the experts' feedback, and present the lessons learned.

2015, Keim, Daniel A., Schreck, Tobias

2014, Mittelstädt, Sebastian, Stoffel, Andreas, Schreck, Tobias, Keim, Daniel A.

Color, after position, is among the most effective visual variables to encode information. It is pre-attentively processed by the visual system, and if used appropriately, supports detection and correlation of patterns. Several global color mapping schemes (such as linear, non-linear and histogram-based) exist that support certain analysis tasks. However, static global schemes map data with a small local variation (within a data set of high variation) to small color differences. Often, these color differences are below the noticeable difference threshold of user perception or the display device. As a consequence, valuable information may be lost since data points or structures cannot be adequately perceived and correlations or other patterns of interest may be missed. Existing techniques to avoid this effect either require user interaction or are based on specific assumptions about the data. We introduce a novel automatic algorithm for local-adaptive color mapping that is applicable to dense data and is based on the idea to locally modify the color mapping to enhance the visibility of structures. This technique emphasizes patterns of interest within locally chosen color-ranges such that (1) the visibility of local differences is enhanced and (2) the introduced global distortion of the color mapping is kept small. This allows the perception of relevant patterns while approximately maintaining global comparability across the whole data set.

2012-10, Tatu, Andrada, Maaß, Fabian, Färber, Ines, Bertini, Enrico, Schreck, Tobias, Seidl, Thomas, Keim, Daniel A.

In explorative data analysis, the data under consideration often resides in a high-dimensional (HD) data space. Currently many methods are available to analyze this type of data. So far, proposed automatic approaches include dimensionality reduction and cluster analysis, whereby visual-interactive methods aim to provide effective visual mappings to show, relate, and navigate HD data. Furthermore, almost all of these methods conduct the analysis from a singular perspective, meaning that they consider the data in either the original HD data space, or a reduced version thereof. Additionally, HD data spaces often consist of combined features that measure different properties, in which case the particular relationships between the various properties may not be clear to the analysts a priori since it can only be revealed if appropriate feature combinations (subspaces) of the data are taken into consideration. Considering just a single subspace is, however, often not sufficient since different subspaces may show complementary, conjointly, or contradicting relations between data items. Useful information may consequently remain embedded in sets of subspaces of a given HD input data space. Relying on the notion of subspaces, we propose a novel method for the visual analysis of HD data in which we employ an interestingness-guided subspace search algorithm to detect a candidate set of subspaces. Based on appropriately defined subspace similarity functions, we visualize the subspaces and provide navigation facilities to interactively explore large sets of subspaces. Our approach allows users to effectively compare and relate subspaces with respect to involved dimensions and clusters of objects. We apply our approach to synthetic and real data sets. We thereby demonstrate its support for understanding HD data from different perspectives, effectively yielding a more complete view on HD data.

2015, Hao, Ming C., Schreck, Tobias, Dayal, Umeshwar, Keim, Daniel A.

A collection of data values is divided into plural bins, wherein a number of the bins is dependent upon a change feature of the data values. Parameter values associated with respective bins are determined. Based on the parameter values, visualization of the plural bins is provided in corresponding plural display screen partitions of a display screen, wherein at least one of the display screen partitions has a resolution that is different from another of the display screen partitions.

2014, Shao, Lin, Behrisch, Michael, Schreck, Tobias, Landesberger, Tatjana von, Scherer, Maximilian, Bremm, Sebastian, Keim, Daniel A.

Recently, there has been an interest in methods for filtering large scatter plot spaces for interesting patterns. However, user interaction remains crucial in starting an explorative analysis in a large scatter plot space. We introduce an approach for explorative search and navigation in large sets of scatter plot diagrams. By means of a sketch-based query interface, users can start the exploration process by providing a visual example of the pattern they are interested in. A shadow-drawing approach provides suggestions for possibly relevant patterns while query drawing takes place, supporting the visual search process. We apply the approach on a large real-world data set, demonstrating the principal functionality and usefulness of our technique.

2014, Shao, Lin, Behrisch, Michael, Schreck, Tobias, Sipiran, Ivan, Kwon, Bum Chul, Keim, Daniel A.

Scatter plots are effective diagrams to visualize distributions, clusters and correlations in two-dimensional data space. For highdimensional data, scatter plot matrices can be formed to show all two-dimensional combinations of dimensions. Several previous approaches for exploration of large scatter plot spaces have focused on ranking and sorting scatter plot matrices based on global patterns. However, often local patterns are of interest for scatter plot exploration. We present a preliminary idea to explore the scatter plot space by identifying significant local patterns (also called motifs in this work). Based on certain clustering algorithms and image-based descriptors, we identify and group a set of similar local candidate motifs in a large scatter plot space.

2015, Shao, Lin, Schleicher, Timo, Behrisch, Michael, Schreck, Tobias, Sipiran, Ivan, Keim, Daniel A.

Finding interesting patterns in large scatter plot spaces is a challenging problem and becomes even more difficult with increasing number of dimensions. Previous approaches for exploring large scatter plot spaces like e.g., the well-known Scagnostics approach, mainly focus on ranking scatter plots based on their global properties. However, often local patterns contribute significantly to the interestingness of a scatter plot. We are proposing a novel approach for the automatic determination of interesting views in scatter plot spaces based on analysis of local scatter plot segments. Specifically, we automatically classify similar local scatter plot segments, which we call scatter plot motifs. Inspired by the well-known tf-idf approach from information retrieval, we compute local and global quality measures based on certain frequency properties of the local motifs. We show how we can use these to filter, rank and compare scatter plots and their incorporated motifs. We demonstrate the usefulness of our approach with synthetic and real-world data sets and showcase our corresponding data exploration tool that visualizes the distribution of local scatter plot motifs in relation to a large overall scatter plot space.

2014, Janetzko, Halldor, Sacha, Dominik, Schreck, Tobias, Keim, Daniel A., Deussen, Oliver

Soccer is one the most popular sports today and also very interesting from an scientific point of view. We present a system for analyzing high-frequency position-based soccer data at various levels of detail, allowing to interactively explore and analyze for movement features and game events. Our Visual Analytics method covers single-player, multi-player and event-based analytical views. Depending on the task the most promising features are semi-automatically selected, processed, and visualized. Our aim is to help soccer analysts in finding the most important and interesting events in a match. We present a flexible, modular, and expandable layer-based system allowing in-depth analysis. The integration of Visual Analytics techniques into the analysis process enables the analyst to find interesting events based on classification and allows, by a set of custom views, to communicate the found results. The feedback loop in the Visual Analytics pipeline helps to further improve the classification results. We evaluate our approach by investigating real-world soccer matches and collecting additional expert feedback. Several use cases and findings illustrate the capabilities of our approach.

2014, Behrisch, Michael, Shao, Lin, Kwon, Bum Chul, Schreck, Tobias, Sipiran, Ivan, Keim, Daniel A.

Extracting meaningful information out of vast amounts of highdimensional data is very difficult. Prior research studies have been trying to solve these problems through either automatic data analysis or interactive visualization approaches. Our grand goal is to derive the representative and generalizable quality metrics and to apply the metrics to amplify interesting patterns as well as to mute the uninteresting noise for multidimensional visualizations. In this particular poster, we investigate quality metrics driven approach to achieve the goal for scatterplot matrix (SPLOM). Our main approach is to rearrange scatterplot matrices by sorting scatterplots based upon their patterns especially locally significant ones, called scatterplot motifs. Using the approach, we expect scatterplot matrices to reveal groups of visual patterns appearing adjacent to each other, which helps analysts to gain a clear overview and to delve into specific areas of interest more easily. Our ongoing investigation aims to test and refine the feature vector for scatterplot motifs depending upon data sizes and the number of dimensions.