Computational geometry

The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and pre-processing data, engineering new features, and creating and scoring models based on a target objectives (e.g. accuracy or run-time efficiency). Though not yet widely adopted, we are interested in understanding how AutoAI will impact the practice of data science. We conducted interviews with 20 data scientists who work at a large, multinational technology company and practice data science in various business settings. Our goal is to understand their current work practices and how these practices might change with AutoAI. Reactions were mixed: while informants expressed concerns about the trend of automating their jobs, they also strongly felt it was inevitable. Despite these concerns, they remained optimistic about their future job security due to a view that the future of data science work will be a collaboration between humans and AI systems, in which both automation and human expertise are indispensable.

Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI

Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer

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Effective injury forecasting in soccer with GPS training data and machine learning.

Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. However, we still lack a deep understanding of how data science workers collaborate in practice. In this work, we conducted an online survey with 183 participants who work in various aspects of data science. We focused on their reported interactions with each other (e.g., managers with engineers) and with different tools (e.g., Jupyter Notebook). We found that data science teams are extremely collaborative and work with a variety of stakeholders and tools during the six common steps of a data science workflow (e.g., clean data and train model). We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use. Based on these findings, we discuss design implications for supporting data science team collaborations and future research directions.

How do Data Science Workers Collaborate? Roles, Workflows, and Tools

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.

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Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis

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.

How to make sense of team sport data: From acquisition to data modeling and research aspects

Outage management in electrical networks is a complex task for operators and requires comprehensive overviews of the topology. At the same time valuable information for detecting the root cause may have geographical context such as digging activities or falling trees. Consequently, vendors of state-of-the-art SCADA systems started to integrate this valuable information source as well. However, in todays systems both views are separated, requiring operators to mentally connect the geographical and topological information. The wish of operators is to provide a comprehensive combination of both spaces in a single view. However, how to project geographical elements into the topology to support the workflow of real operators is yet unclear. In this paper, we present a design study for an interactive visualization system that provides a comprehensive overview for power grid operators. It provides full coverage of both spaces in order to measure how real operators make use of the geographical information. It bypasses the projection problem by interactive brushing-and-linking to support associative analysis. We extracted the mental-model of domain experts in real use cases and found a general bias source in sequential analysis of two spaces. We contribute our problem and task abstraction, lessons learned, and implications for future research.

Lessons on combining topology and geography: visual analytics for electrical outage management

VISONE (Visual Social Networks) is an open-source tool, developed at the University of Konstanz, to analyse and visualize graph structures. KNIME (Konstanz Information Miner) is a open-source tool, developed at the University of Konstanz, containing different Data Mining techniques and the possibility to extract trained models in the PMML (Predictive Model Markup Language) format in order to import them into other applications.

MooVis -- A Visual Analytics Tool for the Prediction of Movie Viewer Ratings and Boxoffice

The invention relates to a transport and distribution network (100), comprising: a plurality of individual components (101-1, …, 101-n) each having a data interface (103-1, …, 103-n) for transferring status data (113) of the individual component (101-1, …, 101-n); and a central evaluating device (105) for calculating a failure probability of the individual component (101-1, …, 101-n) on the basis of the status data (113).

Transport and distribution network

Supervising a large complex environment is quite challenging due to the high amount of data incoming each second and asking for the operators’ attention. We increase the situational awareness by fusing events, tasks, and spatial awareness employing a combination of the visual and the acoustical channel. Our control room setup allows collaborative hypothesis generation and verification in a shared working environment. Global views can be put on each operator’s touch screen for detailed analyses and annotations. All information is shared automatically between all operators.

Alexander Jäger

Papers

How to make sense of team sport data: From acquisition to data modeling and research aspects

Lessons on combining topology and geography: visual analytics for electrical outage management

MooVis -- A Visual Analytics Tool for the Prediction of Movie Viewer Ratings and Boxoffice

Transport and distribution network

Fusing Events, Tasks and Spatial Awareness in an Ambient-Enabled Work Environment