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Auflistung nach Autor:in "Brandt, Tobias"

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  • Zeitschriftenartikel
    Call for Papers: Issue 3/2018
    (Business & Information Systems Engineering: Vol. 58, No. 6, 2016) Brandt, Tobias; Ketter, Wolfgang; Kolbe, Lutz M.; Neumann, Dirk; Watson, Richard T.
  • Konferenzbeitrag
    Herakles: A system for sensor-based live sport analysis using private peer-to-peer networks
    (Datenbanksysteme für Business, Technologie und Web (BTW 2015) - Workshopband, 2015) Brand, Michael; Brandt, Tobias; Cordes, Carsten; Wilken, Marc; Michelsen, Timo
    Tactical decisions characterize team sports like soccer or basketball profoundly. Analyses of training sessions and matches (e.g., mileage or pass completion rate of a player) form more and more a crucial base for those tactical decisions. Most of the analyses are video-based, resulting in high operating expenses. Additionally, a highly specialized system with a huge amount of system resources like processors and memory is needed. Typically, analysts present the results of the video data in time-outs (e.g., in the half-time break of a soccer match). Therefore, coaches are not able to view statistics during the match. In this paper we propose the concepts and current state of Herakles, a system for live sport analysis which uses streaming sensor data and a Peer-to-Peer network of conventional and low-cost private machines. Since sensor data is typically of high volume and velocity, we use a distributed data stream management system (DSMS). The results of the data stream processing are intended for coaches. Therefore, the front-end of Herakles is an application for mobile devices (like smartphones or tablets). Each device is connected with the distributed DSMS, retrieves updates of the results and presents them in real-time. Therefore, Herakles enables the coach to analyze his team during the match and to react immediately with tactical decisions.
  • Zeitschriftenartikel
    Informationsunschärfe in Big Data
    (Wirtschaftsinformatik: Vol. 56, No. 5, 2014) Bendler, Johannes; Wagner, Sebastian; Brandt, Tobias; Neumann, Dirk
    Während die klassische Definition von Big Data ursprünglich nur die drei Größen Datenmenge (Volume), Datenrate (Velocity) und Datenvielfalt (Variety) umfasste, ist in jüngster Zeit der Wahrheitsgehalt (Veracity) als weitere Dimension mehr und mehr in den wissenschaftlichen und praktischen Fokus gerückt. Der noch immer wachsende Bereich der Sozialen Medien und damit verbundene benutzergenerierte Datenmengen verlangen nach neuen Methoden, die die enthaltene Datenunschärfe abschätzen und kontrollieren können. Dieser Beitrag widmet sich einem Aspekt der Datenunschärfe und stellt einen neuartigen Ansatz vor, der die Verlässlichkeit von benutzergenerierten Daten auf Basis von wiederkehrenden Mustern abschätzt. Zu diesem Zweck wird eine große Menge von Twitter-Statusnachrichten mit geographischer Standortinformation aus San Francisco untersucht und mit Points of Interest (POIs), wie beispielsweise Bars, Restaurants oder Parks, in Verbindung gebracht. Das vorgeschlagene Modell wird durch kausale Beziehungen zwischen Points of Interest und den in der Umgebung vorliegenden Twitter-Meldungen validiert. Weiterhin wird die zeitliche Dimension dieser Beziehung in Betracht gezogen, um so in Abhängigkeit der Art des POI wiederkehrende Muster zu identifizieren. Die durchgeführten Analysen münden in einem Indikator, der die Verlässlichkeit von vorliegenden Daten in räumlicher und zeitlicher Dimension abschätzt.AbstractWhile the classic definition of Big Data included the dimensions volume, velocity, and variety, a fourth dimension, veracity, has recently come to the attention of researchers and practitioners. The increasing amount of user-generated data associated with the rise of social media emphasizes the need for methods to deal with the uncertainty inherent to these data sources. In this paper we address one aspect of uncertainty by developing a new methodology to establish the reliability of user-generated data based upon causal links with recurring patterns. We associate a large data set of geo-tagged Twitter messages in San Francisco with points of interest, such as bars, restaurants, or museums, within the city. This model is validated by causal relationships between a point of interest and the amount of messages in its vicinity. We subsequently analyze the behavior of these messages over time using a jackknifing procedure to identify categories of points of interest that exhibit consistent patterns over time. Ultimately, we condense this analysis into an indicator that gives evidence on the certainty of a data set based on these causal relationships and recurring patterns in temporal and spatial dimensions.
  • Zeitschriftenartikel
    Intermodal Mobility
    (Business & Information Systems Engineering: Vol. 59, No. 3, 2017) Willing, Christoph; Brandt, Tobias; Neumann, Dirk
  • Zeitschriftenartikel
    Interview with David Prendergast on "Mediating Between Technology and People in Smart City Transformations"
    (Business & Information Systems Engineering: Vol. 60, No. 3, 2018) Brandt, Tobias
  • Zeitschriftenartikel
    Smart Cities and Digitized Urban Management
    (Business & Information Systems Engineering: Vol. 60, No. 3, 2018) Brandt, Tobias; Ketter, Wolf; Kolbe, Lutz M.; Neumann, Dirk; Watson, Richard T.