Prof. Dr.

Wrobel, Stefan

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Visual Analytics for Data Scientists

2020 , Andrienko, Natalia , Andrienko, Gennady , Fuchs, Georg , Slingsby, Aidan , Turkay, Cagatay , Wrobel, Stefan

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Geovisual analytics for spatial decision support

2007 , Andrienko, Gennady , Andrienko, Natalia , Jankowski, P. , Keim, Daniel A. , Kraak, M.-J. , 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.

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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.

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Movement data anonymity through generalization

2010 , Monreale, Anna , Andrienko, Gennady , Andrienko, Natalia , Giannotti, Fosca , 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.

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