2022-08-25T08:27:36Z, Cakmak, Eren, Fuchs, Johannes, Jäckle, Dominik, Schreck, Tobias, Brandes, Ulrik, Keim, Daniel A.

Many data analysis problems rely on dynamic networks, such as social or communication network analyses. Providing a scalable overview of long sequences of such dynamic networks remains challenging due to the underlying large-scale data containing elusive topological changes. We propose two complementary pixel-based visualizations, which reflect occurrences of selected sub-networks (motifs) and provide a time-scalable overview of dynamic networks: a network-level census (motif significance profiles) linked with a node-level sub-network metric (graphlet degree vectors) views to reveal structural changes, trends, states, and outliers. The network census captures significantly occurring motifs compared to their expected occurrences in random networks and exposes structural changes in a dynamic network. The sub-network metrics display the local topological neighborhood of a node in a single network belonging to the dynamic network. The linked pixel-based visualizations allow exploring motifs in different-sized networks to analyze the changing structures within and across dynamic networks, for instance, to visually analyze the shape and rate of changes in the network topology. We describe the identification of visual patterns, also considering different reordering strategies to emphasize visual patterns. We demonstrate the approach's usefulness by a use case analysis based on real-world large-scale dynamic networks, such as the evolving social networks of Reddit or Facebook.

2018, Häußler, Johannes, Stein, Manuel, Seebacher, Daniel, Janetzko, Halldor, Schreck, Tobias, Keim, Daniel A.

Urbanization is an increasing global trend resulting in a strong increase in public and individual transportation needs. Accordingly, a major challenge for traffic and urban planners is the design of sustainable mobility concepts to maintain and increase the long-term health of humans by reducing environmental pollution. Recent developments in sensor technology allow the precise tracking of vehicle sensor information, allowing a closer and more in-depth analysis of traffic data. We propose a visual analytics system for the exploration of environmental factors in these high-resolution and high-dimensional mobility sensor data. Additionally, we introduce an interactive visual logging approach to enable experts to cope with complex interactive analysis processes and the problem of the reproducibility of results. The usefulness of our approach is demonstrated via two expert studies with two domain experts from the field of environment-related projects and urban traffic planning.

2017-07-04, Sacha, Dominik, Al-Masoudi, Feeras, Stein, Manuel, Schreck, Tobias, Keim, Daniel A., Andrienko, Gennady, Janetzko, Halldor

Trajectory-based visualization of coordinated movement data within a bounded area, such as player and ball movement within a soccer pitch, can easily result in visual crossings, overplotting, and clutter. Trajectory abstraction can help to cope with these issues, but it is a challenging problem to select the right level of abstraction (LoA) for a given data set and analysis task. We present a novel dynamic approach that combines trajectory simplification and clustering techniques with the goal to support interpretation and understanding of movement patterns. Our technique provides smooth transitions between different abstraction types that can be computed dynamically and on-the-fly. This enables the analyst to effectively navigate and explore the space of possible abstractions in large trajectory data sets. Additionally, we provide a proof of concept for supporting the analyst in determining the LoA semi-automatically with a recommender system. Our approach is illustrated and evaluated by case studies, quantitative measures, and expert feedback. We further demonstrate that it allows analysts to solve a variety of analysis tasks in the domain of soccer.

2016, Stein, Manuel, Janetzko, Halldor, Lamprecht, Andreas, Seebacher, Daniel, Schreck, Tobias, Keim, Daniel A., Grossniklaus, Michael

Sport analytics in general and soccer analytics in particular constitute quickly growing markets when it comes to professional analyses and visualizations. From a data analysis research perspective, soccer is a rich source of geospatial and temporal movement data, with high details and a controlled environment. However, soccer movement is complex as its compounds are actions and reactions of two opposing teams with inverse goals. Common analyses performed today are typically oriented towards statistical analysis and considering aggregate measurements. In this work, we propose a set of effective visual-interactive methods for investigating set plays as a first step towards semi-automated analysis of tactic behavior. In our analytic design, we follow the so-called Information-seeking Mantra by Ben Shneiderman by providing overview visualizations, interactive refinements, and detailed analysis views. We take an applied approach in showing case studies that give evidence for the applicability and merits of our proposed techniques.

2020, Cakmak, Eren, Jäckle, Dominik, Schreck, Tobias, Keim, Daniel A.

Presenting long sequences of dynamic graphs remains challenging due to the underlying large-scale and high-dimensional data. We propose dg2pix, a novel pixel-based visualization technique, to visually explore temporal and structural properties in long sequences of large-scale graphs. The approach consists of three main steps: (1) the multiscale modeling of the temporal dimension; (2) unsupervised graph embeddings to learn low-dimensional representations of the dynamic graph data; and (3) an interactive pixel-based visualization to simultaneously explore the evolving data at different temporal aggregation scales. dg2pix provides a scalable overview of a dynamic graph, supports the exploration of long sequences of high-dimensional graph data, and enables the identification and comparison of similar temporal states. We show the applicability of the technique to synthetic and real-world datasets, demonstrating that temporal patterns in dynamic graphs can be identified and interpreted over time. dg2pix contributes a suitable intermediate representation between node-link diagrams at the high detail end and matrix representations on the low detail end.

2018, Stein, Manuel, Janetzko, Halldor, Schreck, Tobias, Keim, Daniel A.

Analysts and coaches in soccer sports need to investigate large sets of past matches of opposing teams in short time to prepare their teams for upcoming matches. Thus, they need appropriate methods and systems supporting them in searching for soccer moves for comparison and explanation. For the search of similar soccer moves, established distance and similarity measures typically only take spatio-temporal features like shape and speed of movement into account. However, movement in invasive team sports such as soccer, includes much more than just a sequence of spatial locations. We survey the current state-of-the-art in trajectory distance measures and subsequently propose an enhanced similarity measure integrating spatial, player, event as well as high level context such as pressure into the process of similarity search. We present a visual search system supporting analysts in interactively identifying similar contextual enhanced soccer moves in a dataset containing more than 60 soccer matches. Our approach is evaluated by several expert studies. The results of the evaluation reveal the large potential of enhanced similarity measures in the future.

2017, Jäckle, Dominik, Blumenschein, Michael, Behrisch, Michael, Keim, Daniel A., Schreck, Tobias

Subspace analysis methods have gained interest for identifying patterns in subspaces of high-dimensional data. Existing techniques allow to visualize and compare patterns in subspaces. However, many subspace analysis methods produce an abundant amount of patterns, which often remain redundant and are difficult to relate. Creating effective layouts for comparison of subspace patterns remains challenging. We introduce Pattern Trails, a novel approach for visually ordering and comparing subspace patterns. Central to our approach is the notion of pattern transitions as an interpretable structure imposed to order and compare patterns between subspaces. The basic idea is to visualize projections of subspaces side-by-side, and indicate changes between adjacent patterns in the subspaces by a linked representation, hence introducing pattern transitions. Our contributions comprise a systematization for how pairs of subspace patterns can be compared, and how changes can be interpreted in terms of pattern transitions. We also contribute a technique for visual subspace analysis based on a data-driven similarity measure between subspace representations. This measure is useful to order the patterns, and interactively group subspaces to reduce redundancy. We demonstrate the usefulness of our approach by application to several use cases, indicating that data can be meaningfully ordered and interpreted in terms of pattern transitions.

2018-10, Stein, Manuel, Breitkreutz, Thorsten, Häußler, Johannes, Seebacher, Daniel, Niederberger, Christoph, Schreck, Tobias, Grossniklaus, Michael, Keim, Daniel A., Janetzko, Halldor

The analysis of invasive team sports often concentrates on cooperative and competitive aspects of collective movement behavior. A main goal is the identification and explanation of strategies, and eventually the development of new strategies. In visual sports analytics, a range of different visual-interactive analysis techniques have been proposed, e.g., based on visualization using for example trajectories, graphs, heatmaps, and animations. Identifying suitable visualizations for a specific situation is key to a successful analysis. Existing systems enable the interactive selection of different visualization facets to support the analysis process. However, an interactive selection of appropriate visualizations is a difficult, complex, and time-consuming task. In this paper, we propose a four-step analytics conceptual workflow for an automatic selection of appropriate views for key situations in soccer games. Our concept covers classification, specification, explanation, and alteration of match situations, effectively enabling the analysts to focus on important game situations and the determination of alternative moves. Combining abstract visualizations with real world video recordings by Immersive Visual Analytics and descriptive storylines, we support domain experts in understanding key situations. We demonstrate the usefulness of our proposed conceptual workflow via two proofs of concept and evaluate our system by comparing our results to manual video annotations by domain experts. Initial expert feedback shows that our proposed concept improves the understanding of competitive sports and leads to a more efficient data analysis.

2018, Jentner, Wolfgang, Jäckle, Dominik, Engelke, Ulrich, Keim, Daniel A., Schreck, Tobias

2016, Blumenschein, Michael, Färber, Ines, Behrisch, Michael, Tatu, Andrada, Schreck, Tobias, Keim, Daniel A., Seidl, Thomas

The quality assessment of results of clustering algorithms is challenging as different cluster methodologies lead to different cluster characteristics and topologies. A further complication is that in high-dimensional data, subspace clustering adds to the complexity by detecting clusters in multiple different lower-dimensional projections. The quality assessment for (subspace) clustering is especially difficult if no benchmark data is available to compare the clustering results. In this research paper, we present SubEval, a novel subspace evaluation framework, which provides visual support for comparing quality criteria of subspace clusterings. We identify important aspects for evaluation of subspace clustering results and show how our system helps to derive quality assessments. SubEval allows assessing subspace cluster quality at three different granularity levels: (1) A global overview of similarity of clusters and estimated redundancy in cluster members and subspace dimensions. (2) A view of a selection of multiple clusters supports in-depth analysis of object distributions and potential cluster overlap. (3) The detail analysis of characteristics of individual clusters helps to understand the (non-)validity of a cluster. We demonstrate the usefulness of SubEval in two case studies focusing on the targeted algorithm- and domain scientists and show how the generated insights lead to a justified selection of an appropriate clustering algorithm and an improved parameter setting. Likewise, SubEval can be used for the understanding and improvement of newly developed subspace clustering algorithms. SubEval is part of SubVA, a novel open-source web-based framework for the visual analysis of different subspace analysis techniques.