Prof. Dr.

Wrobel, Stefan

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  • Publication
    Visual Analytics for Human-Centered Machine Learning
    ( 2022-01-25)
    Andrienko, Natalia
    ;
    Andrienko, Gennady
    ;
    Adilova, Linara
    ;
    Wrobel, Stefan
    We introduce a new research area in visual analytics (VA) aiming to bridge existing gaps between methods of interactive machine learning (ML) and eXplainable Artificial Intelligence (XAI), on one side, and human minds, on the other side. The gaps are, first, a conceptual mismatch between ML/XAI outputs and human mental models and ways of reasoning, and second, a mismatch between the information quantity and level of detail and human capabilities to perceive and understand. A grand challenge is to adapt ML and XAI to human goals, concepts, values, and ways of thinking. Complementing the current efforts in XAI towards solving this challenge, VA can contribute by exploiting the potential of visualization as an effective way of communicating information to humans and a strong trigger of human abstractive perception and thinking. We propose a cross-disciplinary research framework and formulate research directions for VA.
  • Publication
    A theoretical model for pattern discovery in visual analytics
    (Elsevier B.V., 2021-01-21)
    Andrienko, Natalia
    ;
    Andrienko, Gennady
    ;
    Miksch, Silvia
    ;
    Schumann, Heidrun
    ;
    Wrobel, Stefan
    The word 'pattern' frequently appears in the visualisation and visual analytics literature, but what do we mean when we talk about patterns? We propose a practicable definition of the concept of a pattern in a data distribution as a combination of multiple interrelated elements of two or more data components that can be represented and treated as a unified whole. Our theoretical model describes how patterns are made by relationships existing between data elements. Knowing the types of these relationships, it is possible to predict what kinds of patterns may exist. We demonstrate how our model underpins and refines the established fundamental principles of visualisation. The model also suggests a range of interactive analytical operations that can support visual analytics workflows where patterns, once discovered, are explicitly involved in further data analysis.
  • Publication
    Constructing Spaces and Times for Tactical Analysis in Football
    ( 2021)
    Andrienko, Gennady
    ;
    Andrienko, Natalia
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    Anzer, Gabriel
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    Bauer, Pascal
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    Budziak, Guido
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    Fuchs, Georg
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    Hecker, Dirk
    ;
    Weber, Hendrik
    ;
    Wrobel, Stefan
    A possible objective in analyzing trajectories of multiple simultaneously moving objects, such as football players during a game, is to extract and understand the general patterns of coordinated movement in different classes of situations as they develop. For achieving this objective, we propose an approach that includes a combination of query techniques for flexible selection of episodes of situation development, a method for dynamic aggregation of data from selected groups of episodes, and a data structure for representing the aggregates that enables their exploration and use in further analysis. The aggregation, which is meant to abstract general movement patterns, involves construction of new time-homomorphic reference systems owing to iterative application of aggregation operators to a sequence of data selections. As similar patterns may occur at different spatial locations, we also propose constructing new spatial reference systems for aligning and matching movements irrespective of their absolute locations. The approach was tested in application to tracking data from two Bundesliga games of the 2018/2019 season. It enabled detection of interesting and meaningful general patterns of team behaviors in three classes of situations defined by football experts. The experts found the approach and the underlying concepts worth implementing in tools for football analysts.
  • Publication
    Visual Analytics for Data Scientists
    (Springer Nature, 2020)
    Andrienko, Natalia
    ;
    Andrienko, Gennady
    ;
    Fuchs, Georg
    ;
    Slingsby, Aidan
    ;
    Turkay, Cagatay
    ;
    Wrobel, Stefan
  • Publication
    Scalable analysis of movement data for extracting and exploring significant places
    ( 2013)
    Andrienko, Gennady
    ;
    Andrienko, Natalia
    ;
    Hurter, C.
    ;
    Rinzivillo, Salvatore
    ;
    Wrobel, Stefan
    Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these places and, possibly, other characteristics of the places and links between them. For this class of problems, we propose a visual analytics procedure consisting of four major steps: 1) event extraction from trajectories; 2) extraction of relevant places based on event clustering; 3) spatiotemporal aggregation of events or trajectories; 4) analysis of the aggregated data. All steps can be fulfilled in a scalable way with respect to the amount of the data under analysis; therefore, the procedure is not limited by the size of the computer's RAM and can be applied to very large data sets. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales.
  • Publication
    Visual analytics of movement
    (Springer, 2013)
    Andrienko, Gennady
    ;
    Andrienko, Natalia
    ;
    Bak, P.
    ;
    Keim, D.
    ;
    Wrobel, Stefan
    Many important planning decisions in society and business depend on proper knowledge and a correct understanding of movement, be it in transportation, logistics, biology, or the life sciences. Today the widespread use of mobile phones and technologies like GPS and RFID provides an immense amount of data on location and movement. What is needed are new methods of visualization and algorithmic data analysis that are tightly integrated and complement each other to allow end-users and analysts to extract useful knowledge from these extremely large data volumes. This is exactly the topic of this book. As the authors show, modern visual analytics techniques are ready to tackle the enormous challenges brought about by movement data, and the technology and software needed to exploit them are available today. The authors start by illustrating the different kinds of data available to describe movement, from individual trajectories of single objects to multiple trajectories of man y objects, and then proceed to detail a conceptual framework, which provides the basis for a fundamental understanding of movement data. With this basis, they move on to more practical and technical aspects, focusing on how to transform movement data to make it more useful, and on the infrastructure necessary for performing visual analytics in practice. In so doing they demonstrate that visual analytics of movement data can yield exciting insights into the behavior of moving persons and objects, but can also lead to an understanding of the events that transpire when things move. Throughout the book, they use sample applications from various domains and illustrate the examples with graphical depictions of both the interactive displays and the analysis results. In summary, readers will benefit from this detailed description of the state of the art in visual analytics in various ways. Researchers will appreciate the scientific precision involved, software technologists will find essential information o
  • Publication
    From movement tracks through events to places: Extracting and characterizing significant places from mobility data
    ( 2011)
    Andrienko, Gennady
    ;
    Andrienko, Natalia
    ;
    Hurter, Christophe
    ;
    Rinzivillo, Salvatore
    ;
    Wrobel, Stefan
    We propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two realworld problems requiring analysis at different spatial scales.
  • Publication
    Movement data anonymity through generalization
    ( 2010)
    Monreale, Anna
    ;
    Andrienko, Gennady
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    Andrienko, Natalia
    ;
    Giannotti, Fosca
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    Pedreschi, Dino
    ;
    Rinzivillo, Salvatore
    ;
    Wrobel, Stefan
    Wireless networks and mobile devices, such as mobile phones and GPS receivers, sense and track the movements of people and vehicles, producing society-wide mobility databases. This is a challenging scenario for data analysis and mining. On the one hand, exciting opportunities arise out of discovering new knowledge about human mobile behavior, and thus fuel intelligent info-mobility applications. On other hand, new privacy concerns arise when mobility data are published. The risk is particularly high for GPS trajectories, which represent movement of a very high precision and spatio-temporal resolution: the de-identification of such trajectories (i.e., forgetting the ID of their associated owners) is only a weak protection, as generally it is possible to re-identify a person by observing her routine movements. In this paper we propose a method for achieving true anonymity in a dataset of published trajectories, by defining a transformation of the original GPS trajectories based on spatial generalization and k-anonymity. The proposed method offers a formal data protection safeguard, quantified as a theoretical upper bound to the probability of re-identification. We conduct a thorough study on a real-life GPS trajectory dataset, and provide strong empirical evidence that the proposed anonymity techniques achieve the conflicting goals of data utility and data privacy. In practice, the achieved anonymity protection is much stronger than the theoretical worst case, while the quality of the cluster analysis on the trajectory data is preserved.
  • Publication
    Geovisual analytics for spatial decision support
    ( 2007)
    Andrienko, Gennady
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    Andrienko, Natalia
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    Jankowski, P.
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    Keim, Daniel A.
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    Kraak, M.-J.
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    MacEachren, Alan M.
    ;
    Wrobel, Stefan
    This article summarizes the results of the workshop on Visualization, Analytics & Spatial Decision Support, which took place at the GIScience conference in September 2006. The discussions at the workshop and analysis of the state of the art have revealed a need in concerted cross-disciplinary efforts to achieve substantial progress in supporting space-related decision making. The size and complexity of real-life problems together with their ill-defined nature call for a true synergy between the power of computational techniques and the human capabilities to analyze, envision, reason, and deliberate. Existing methods and tools are yet far from enabling this synergy. Appropriate methods can only appear as a result of a focused research based on the achievements in the fields of geovisualization and information visualization, human-computer interaction, geographic information science, operations research, data mining and machine learning, decision science, cognitive science, and other disciplines. The name 'Geovisual Analytics for Spatial Decision Support' suggested for this new research direction emphasizes the importance of visualization and interactive visual interfaces and the link with the emerging research discipline of Visual Analytics. This article, as well as the whole special issue, is meant to attract the attention of scientists with relevant expertise and interests to the major challenges requiring multidisciplinary efforts and to promote the establishment of a dedicated research community where an appropriate range of competences is combined with an appropriate breadth of thinking.