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Manuel Stein
University of Konstanz

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Conference paper
Published: 08 December 2018 in Computer Vision
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Match analysis has become an important task in everyday work at professional soccer clubs in order to improve team performance. Video analysts regularly spend up to several days analyzing and summarizing matches based on tracked and annotated match data. Although there already exists extensive capabilities to track the movement of players and the ball from multimedia data sources such as video recordings, there is no capability to sufficiently detect dynamic and complex events within these data. As a consequence, analysts have to rely on manually created annotations, which are very time-consuming and expensive to create. We propose a novel method for the semi-automatic definition and detection of events based entirely on movement data of players and the ball. Incorporating Allen’s interval algebra into a visual analytics system, we enable analysts to visually define as well as search for complex, hierarchical events. We demonstrate the usefulness of our approach by quantitatively comparing our automatically detected events with manually annotated events from a professional data provider as well as several expert interviews. The results of our evaluation show that the required annotation time for complete matches by using our system can be reduced to a few seconds while achieving a similar level of performance.

ACS Style

Manuel Stein; Daniel Seebacher; Tassilo Karge; Tom Polk; Michael Grossniklaus; Daniel A. Keim. From Movement to Events: Improving Soccer Match Annotations. Computer Vision 2018, 130 -142.

AMA Style

Manuel Stein, Daniel Seebacher, Tassilo Karge, Tom Polk, Michael Grossniklaus, Daniel A. Keim. From Movement to Events: Improving Soccer Match Annotations. Computer Vision. 2018; ():130-142.

Chicago/Turabian Style

Manuel Stein; Daniel Seebacher; Tassilo Karge; Tom Polk; Michael Grossniklaus; Daniel A. Keim. 2018. "From Movement to Events: Improving Soccer Match Annotations." Computer Vision , no. : 130-142.

Journal article
Published: 22 October 2018 in IEEE Transactions on Big Data
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Invasive species are a major cause of ecological damage and commercial losses. A current problem spreading in North America and Europe is the vinegar fly Drosophila suzukii. Unlike other Drosophila, it infests non-rotting and healthy fruits and is therefore of concern to fruit growers, such as vintners. Consequently, large amounts of data about the occurrence of D. suzukii have been collected in recent years. However, there is a lack of interactive methods to investigate this data. We employ ensemble-based classification to predict areas susceptible to the occurrence of D. suzukii and bring them into a spatio-temporal context using maps and glyph-based visualizations. Following the information-seeking mantra, we provide a visual analysis system Drosophigator for spatio-temporal event predictions, enabling the investigation of the spread dynamics of invasive species. We demonstrate the usefulness of our approach in three use cases and an evaluation with more than 30 domain experts.

ACS Style

Daniel Seebacher; Johannes Hausler; Michael Hundt; Manuel Stein; Hannes Muller; Ulrich Engelke; Daniel A. Keim. Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species. IEEE Transactions on Big Data 2018, 7, 497 -509.

AMA Style

Daniel Seebacher, Johannes Hausler, Michael Hundt, Manuel Stein, Hannes Muller, Ulrich Engelke, Daniel A. Keim. Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species. IEEE Transactions on Big Data. 2018; 7 (3):497-509.

Chicago/Turabian Style

Daniel Seebacher; Johannes Hausler; Michael Hundt; Manuel Stein; Hannes Muller; Ulrich Engelke; Daniel A. Keim. 2018. "Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species." IEEE Transactions on Big Data 7, no. 3: 497-509.

Proceedings article
Published: 01 October 2018 in 2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)
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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.

ACS Style

Manuel Stein; Thorsten Breitkreutz; Johannes Haussler; Daniel Seebacher; Christoph Niederberger; Tobias Schreck; Michael Grossniklaus; Daniel Keim; Halldor Janetzko. Revealing the Invisible: Visual Analytics and Explanatory Storytelling for Advanced Team Sport Analysis. 2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) 2018, 1 -9.

AMA Style

Manuel Stein, Thorsten Breitkreutz, Johannes Haussler, Daniel Seebacher, Christoph Niederberger, Tobias Schreck, Michael Grossniklaus, Daniel Keim, Halldor Janetzko. Revealing the Invisible: Visual Analytics and Explanatory Storytelling for Advanced Team Sport Analysis. 2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA). 2018; ():1-9.

Chicago/Turabian Style

Manuel Stein; Thorsten Breitkreutz; Johannes Haussler; Daniel Seebacher; Christoph Niederberger; Tobias Schreck; Michael Grossniklaus; Daniel Keim; Halldor Janetzko. 2018. "Revealing the Invisible: Visual Analytics and Explanatory Storytelling for Advanced Team Sport Analysis." 2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) , no. : 1-9.

Proceedings article
Published: 01 October 2017 in 2017 IEEE Visualization in Data Science (VDS)
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Invasive species are a major cause of ecological damage and commercial losses. A current problem spreading in North America and Europe is the vinegar fly Drosophila suzukii. Unlike other Drosophila, it infests non-rotting and healthy fruits and is therefore of concern to fruitgrowers, such as vintners. Consequently, large amounts of data about infestations have been collected in recent years. However, there is a lack of interactive methods to investigate this data. We employ ensemble-based classification to predict areas susceptible to infestation by D.suzukii and bring them into a spatio-temporal context using maps and glyph-based visualizations. Following the information-seeking mantra, we provide a visual analysis system Drosophigatorfor spatio-temporal event prediction, enabling the investigation of the spread dynamics of invasive species. We demonstrate the usefulness of this approach in two use cases.

ACS Style

Daniel Seebacher; Johannes Hausler; Michael Hundt; Manuel Stein; Hannes Muller; Ulrich Engelke; Daniel Keim. Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species. 2017 IEEE Visualization in Data Science (VDS) 2017, 1 -6.

AMA Style

Daniel Seebacher, Johannes Hausler, Michael Hundt, Manuel Stein, Hannes Muller, Ulrich Engelke, Daniel Keim. Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species. 2017 IEEE Visualization in Data Science (VDS). 2017; ():1-6.

Chicago/Turabian Style

Daniel Seebacher; Johannes Hausler; Michael Hundt; Manuel Stein; Hannes Muller; Ulrich Engelke; Daniel Keim. 2017. "Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species." 2017 IEEE Visualization in Data Science (VDS) , no. : 1-6.

Conference paper
Published: 28 September 2017 in Computer Vision
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A major challenge of the contemporary information age is the overwhelming and increasing data amount, especially when looking for specific information. Searching for relevant information is no longer manually possible, but has to rely on automatic methods, specifically, similarity search. From a formal perspective, similarity search can be seen as the problem of finding entities, which are considered to be similar to a query with respect to certain describing features. The question which features or which weighted combination of features to use for a given query creates a need for semi-automatic methods to address the needs of diverse users. Furthermore, the quality of the results of a similarity search is more than effectiveness, measured by precision and recall. The user ideally needs to trust the results and understand how they were computed. We propose to apply Visual Analytics methodologies, for synergistic cooperation of user and algorithms, to integrate three key dimensions of similarity search: users, tasks, and data for effective search. However, there exists a gap in knowledge how user, task as well as the available data influence each other and the similarity search. In this concept paper, we envision how Visual Analytics can be used to tackle current challenges of similarity search.

ACS Style

Daniel Seebacher; Johannes Häußler; Manuel Stein; Halldor Janetzko; Tobias Schreck; Daniel A. Keim. Visual Analytics and Similarity Search: Concepts and Challenges for Effective Retrieval Considering Users, Tasks, and Data. Computer Vision 2017, 10609, 324 -332.

AMA Style

Daniel Seebacher, Johannes Häußler, Manuel Stein, Halldor Janetzko, Tobias Schreck, Daniel A. Keim. Visual Analytics and Similarity Search: Concepts and Challenges for Effective Retrieval Considering Users, Tasks, and Data. Computer Vision. 2017; 10609 ():324-332.

Chicago/Turabian Style

Daniel Seebacher; Johannes Häußler; Manuel Stein; Halldor Janetzko; Tobias Schreck; Daniel A. Keim. 2017. "Visual Analytics and Similarity Search: Concepts and Challenges for Effective Retrieval Considering Users, Tasks, and Data." Computer Vision 10609, no. : 324-332.

Journal article
Published: 29 August 2017 in IEEE Transactions on Visualization and Computer Graphics
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Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach.

ACS Style

Manuel Stein; Halldor Janetzko; Andreas Lamprecht; Thorsten Breitkreutz; Philipp Zimmermann; Bastian Goldlucke; Tobias Schreck; Gennady Andrienko; Michael Grossniklaus; Daniel A. Keim. Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis. IEEE Transactions on Visualization and Computer Graphics 2017, 24, 13 -22.

AMA Style

Manuel Stein, Halldor Janetzko, Andreas Lamprecht, Thorsten Breitkreutz, Philipp Zimmermann, Bastian Goldlucke, Tobias Schreck, Gennady Andrienko, Michael Grossniklaus, Daniel A. Keim. Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis. IEEE Transactions on Visualization and Computer Graphics. 2017; 24 (1):13-22.

Chicago/Turabian Style

Manuel Stein; Halldor Janetzko; Andreas Lamprecht; Thorsten Breitkreutz; Philipp Zimmermann; Bastian Goldlucke; Tobias Schreck; Gennady Andrienko; Michael Grossniklaus; Daniel A. Keim. 2017. "Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis." IEEE Transactions on Visualization and Computer Graphics 24, no. 1: 13-22.

Journal article
Published: 01 June 2017 in Computer Graphics Forum
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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.

ACS Style

D. Sacha; F. Al‐Masoudi; M. Stein; T. Schreck; D. A. Keim; G. Andrienko; H. Janetzko. Dynamic Visual Abstraction of Soccer Movement. Computer Graphics Forum 2017, 36, 305 -315.

AMA Style

D. Sacha, F. Al‐Masoudi, M. Stein, T. Schreck, D. A. Keim, G. Andrienko, H. Janetzko. Dynamic Visual Abstraction of Soccer Movement. Computer Graphics Forum. 2017; 36 (3):305-315.

Chicago/Turabian Style

D. Sacha; F. Al‐Masoudi; M. Stein; T. Schreck; D. A. Keim; G. Andrienko; H. Janetzko. 2017. "Dynamic Visual Abstraction of Soccer Movement." Computer Graphics Forum 36, no. 3: 305-315.

Journal article
Published: 01 January 2017 in Data
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Automatic and interactive data analysis is instrumental in making use of increasing amounts of complex data. Owing to novel sensor modalities, analysis of data generated in professional team sport leagues such as soccer, baseball, and basketball has recently become of concern, with potentially high commercial and research interest. The analysis of team ball games can serve many goals, e.g., in coaching to understand effects of strategies and tactics, or to derive insights improving performance. Also, it is often decisive to trainers and analysts to understand why a certain movement of a player or groups of players happened, and what the respective influencing factors are. We consider team sport as group movement including collaboration and competition of individuals following specific rule sets. Analyzing team sports is a challenging problem as it involves joint understanding of heterogeneous data perspectives, including high-dimensional, video, and movement data, as well as considering team behavior and rules (constraints) given in the particular team sport. We identify important components of team sport data, exemplified by the soccer case, and explain how to analyze team sport data in general. We identify challenges arising when facing these data sets and we propose a multi-facet view and analysis including pattern detection, context-aware analysis, and visual explanation. We also present applicable methods and technologies covering the heterogeneous aspects in team sport data.

ACS Style

Manuel Stein; Halldór Janetzko; Daniel Seebacher; Alexander Jäger; Manuel Nagel; Jürgen Hölsch; Sven Kosub; Tobias Schreck; Daniel A. Keim; Michael Grossniklaus. How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects. Data 2017, 2, 2 .

AMA Style

Manuel Stein, Halldór Janetzko, Daniel Seebacher, Alexander Jäger, Manuel Nagel, Jürgen Hölsch, Sven Kosub, Tobias Schreck, Daniel A. Keim, Michael Grossniklaus. How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects. Data. 2017; 2 (1):2.

Chicago/Turabian Style

Manuel Stein; Halldór Janetzko; Daniel Seebacher; Alexander Jäger; Manuel Nagel; Jürgen Hölsch; Sven Kosub; Tobias Schreck; Daniel A. Keim; Michael Grossniklaus. 2017. "How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects." Data 2, no. 1: 2.

Conference paper
Published: 31 October 2016 in Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Geographic information systems (GIS) are important for decision support based on spatial data. Due to technical and economical progress an ever increasing number of data sources are available leading to a rapidly growing fast and unreliable amount of data that can be beneficial (1) in the approximation of multivariate and causal predictions of future values as well as (2) in robust and proactive decision-making processes. However, today's GIS are not designed for such big data demands and require new methodologies to effectively model uncertainty and generate meaningful knowledge. As a consequence, we introduce BigGIS, a predictive and prescriptive spatio-temporal analytics platform, that symbiotically combines big data analytics, semantic web technologies and visual analytics methodologies. We present a novel continuous refinement model and show future challenges as an intermediate result of a collaborative research project into big data methodologies for spatio-temporal analysis and design for a big data enabled GIS.

ACS Style

Patrick Wiener; Manuel Stein; Daniel Seebacher; Julian Bruns; Matthias Frank; Viliam Simko; Stefan Zander; Jens Nimis. BigGIS. Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2016, 8 -8:4.

AMA Style

Patrick Wiener, Manuel Stein, Daniel Seebacher, Julian Bruns, Matthias Frank, Viliam Simko, Stefan Zander, Jens Nimis. BigGIS. Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2016; ():8-8:4.

Chicago/Turabian Style

Patrick Wiener; Manuel Stein; Daniel Seebacher; Julian Bruns; Matthias Frank; Viliam Simko; Stefan Zander; Jens Nimis. 2016. "BigGIS." Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems , no. : 8-8:4.

Journal article
Published: 29 September 2016 in IEEE Computer Graphics and Applications
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For development and alignment of tactics and strategies, professional soccer analysts spend up to three working days manually analyzing and annotating professional soccer matches. In an effort to improve soccer player and match analysis, a visual-interactive and data-analysis support system focuses on key situations by using rule-based filtering and automatically annotating key types of soccer match elements. The authors evaluate the proposed approach by analyzing real-world soccer matches and several expert studies. Quantitative measures show the proposed methods can significantly outperform naive solutions.

ACS Style

Manuel Stein; Halldor Janetzko; Thorsten Breitkreutz; Daniel Seebacher; Tobias Schreck; Michael Grossniklaus; Iain Couzin; Daniel A. Keim. Director's Cut: Analysis and Annotation of Soccer Matches. IEEE Computer Graphics and Applications 2016, 36, 50 -60.

AMA Style

Manuel Stein, Halldor Janetzko, Thorsten Breitkreutz, Daniel Seebacher, Tobias Schreck, Michael Grossniklaus, Iain Couzin, Daniel A. Keim. Director's Cut: Analysis and Annotation of Soccer Matches. IEEE Computer Graphics and Applications. 2016; 36 (5):50-60.

Chicago/Turabian Style

Manuel Stein; Halldor Janetzko; Thorsten Breitkreutz; Daniel Seebacher; Tobias Schreck; Michael Grossniklaus; Iain Couzin; Daniel A. Keim. 2016. "Director's Cut: Analysis and Annotation of Soccer Matches." IEEE Computer Graphics and Applications 36, no. 5: 50-60.

Journal article
Published: 14 February 2016 in Electronic Imaging
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ACS Style

Halldór Janetzko; Manuel Stein; Dominik Sacha; Tobias Schreck. Enhancing Parallel Coordinates: Statistical Visualizations for Analyzing Soccer Data. Electronic Imaging 2016, 2016, 1 -8.

AMA Style

Halldór Janetzko, Manuel Stein, Dominik Sacha, Tobias Schreck. Enhancing Parallel Coordinates: Statistical Visualizations for Analyzing Soccer Data. Electronic Imaging. 2016; 2016 (1):1-8.

Chicago/Turabian Style

Halldór Janetzko; Manuel Stein; Dominik Sacha; Tobias Schreck. 2016. "Enhancing Parallel Coordinates: Statistical Visualizations for Analyzing Soccer Data." Electronic Imaging 2016, no. 1: 1-8.

Journal article
Published: 14 February 2016 in Electronic Imaging
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Recently, sports analytics has turned into an important research area of visual analytics and may provide interesting findings, such as the best player of the season, for various kinds of sports. Soccer is a very popular and tactical game, which also attracted great attention in thelast few years. However, the search for complex game movements is a very crucial and challenging task. We present a system for searching trajectory data in soccer matches by means of an interactive search interface that enables the user to sketch a situation of interest. Furthermore, we applya domain specific prefiltering process to extract a set of local movement segments, which are similar to a given sketch. Our approach comprises single-trajectory, multi-trajectory, and event-specific search functions based on two different similarity measures. To demonstrate the usefulnessof our approach, we define a domain specific task analysis and conduct a case study together with a domain expert from FC Bayern München by investigating a real-world soccer match. Finally, we show that multi-trajectory search in combination with event-specific filtering is needed todescribe and retrieve complex moves in soccer matches.

ACS Style

Lin Shao; Dominik Sacha; Benjamin Neldner; Manuel Stein; Tobias Schreck. Visual-Interactive Search for Soccer Trajectories to Identify Interesting Game Situations. Electronic Imaging 2016, 2016, 1 -10.

AMA Style

Lin Shao, Dominik Sacha, Benjamin Neldner, Manuel Stein, Tobias Schreck. Visual-Interactive Search for Soccer Trajectories to Identify Interesting Game Situations. Electronic Imaging. 2016; 2016 (1):1-10.

Chicago/Turabian Style

Lin Shao; Dominik Sacha; Benjamin Neldner; Manuel Stein; Tobias Schreck. 2016. "Visual-Interactive Search for Soccer Trajectories to Identify Interesting Game Situations." Electronic Imaging 2016, no. 1: 1-10.

Journal article
Published: 20 October 2015 in ISPRS International Journal of Geo-Information
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With recent advances in sensor technologies, large amounts of movement data have become available in many application areas. A novel, promising application is the data-driven analysis of team sport. Specifically, soccer matches comprise rich, multivariate movement data at high temporal and geospatial resolution. Capturing and analyzing complex movement patterns and interdependencies between the players with respect to various characteristics is challenging. So far, soccer experts manually post-analyze game situations and depict certain patterns with respect to their experience. We propose a visual analysis system for interactive identification of soccer patterns and situations being of interest to the analyst. Our approach builds on a preliminary system, which is enhanced by semantic features defined together with a soccer domain expert. The system includes a range of useful visualizations to show the ranking of features over time and plots the change of game play situations, both helping the analyst to interpret complex game situations. A novel workflow includes improving the analysis process by a learning stage, taking into account user feedback. We evaluate our approach by analyzing real-world soccer matches, illustrate several use cases and collect additional expert feedback. The resulting findings are discussed with subject matter experts.

ACS Style

Manuel Stein; Johannes Häußler; Dominik Jäckle; Halldór Janetzko; Tobias Schreck; Daniel A. Keim. Visual Soccer Analytics: Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction. ISPRS International Journal of Geo-Information 2015, 4, 2159 -2184.

AMA Style

Manuel Stein, Johannes Häußler, Dominik Jäckle, Halldór Janetzko, Tobias Schreck, Daniel A. Keim. Visual Soccer Analytics: Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction. ISPRS International Journal of Geo-Information. 2015; 4 (4):2159-2184.

Chicago/Turabian Style

Manuel Stein; Johannes Häußler; Dominik Jäckle; Halldór Janetzko; Tobias Schreck; Daniel A. Keim. 2015. "Visual Soccer Analytics: Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction." ISPRS International Journal of Geo-Information 4, no. 4: 2159-2184.

Conference paper
Published: 01 July 2015 in 2015 19th International Conference on Information Visualisation
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Testing is an important and wide spread practice in the development of automotive components. For the design of test methods two types of input data are often considered: (1) load data gathered from real life vehicle fleets, and (2) information of the driving routes based on road features. The development of new technologies is though complicated not only by the need to join those two data sources, but also by the too limited knowledge of the parameters and their useful combinations. As a result, information about representative driving profiles is needed. To address these problems we present a visual analytics approach for analyzing multivariate trajectories as a combination of vehicle's location and road elevation data. Our system combines trajectory clustering, interval-based user-driven trip segmentation, and frequent sequences analysis, supported by contingency table and interval-based Parallel Coordinates visualization and enables the expert user to find representative driving profiles for the definition of very compact test courses.

ACS Style

David Spretke; Manuel Stein; Lyubka Sharalieva; Alexander Warta; Valentin Licht; Tobias Schreck; Daniel A. Keim. Visual Analysis of Car Fleet Trajectories to Find Representative Routes for Automotive Research. 2015 19th International Conference on Information Visualisation 2015, 322 -329.

AMA Style

David Spretke, Manuel Stein, Lyubka Sharalieva, Alexander Warta, Valentin Licht, Tobias Schreck, Daniel A. Keim. Visual Analysis of Car Fleet Trajectories to Find Representative Routes for Automotive Research. 2015 19th International Conference on Information Visualisation. 2015; ():322-329.

Chicago/Turabian Style

David Spretke; Manuel Stein; Lyubka Sharalieva; Alexander Warta; Valentin Licht; Tobias Schreck; Daniel A. Keim. 2015. "Visual Analysis of Car Fleet Trajectories to Find Representative Routes for Automotive Research." 2015 19th International Conference on Information Visualisation , no. : 322-329.