@misc{Richly2019,
  author    = {Richly, Keven},
  title     = {Leveraging spatio-temporal soccer data to define a graphical query language for game recordings},
  series = {IEEE International Conference on Big Data (Big Data)},
  journal   = {IEEE International Conference on Big Data (Big Data)},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-5386-5035-6},
  issn      = {2639-1589},
  doi       = {10.1109/BigData.2018.8622159},
  pages     = {3456 -- 3463},
  year      = {2019},
  abstract  = {For professional soccer clubs, performance and video analysis are an integral part of the preparation and post-processing of games. Coaches, scouts, and video analysts extract information about strengths and weaknesses of their team as well as opponents by manually analyzing video recordings of past games. Since video recordings are an unstructured data source, it is a complex and time-intensive task to find specific game situations and identify similar patterns. In this paper, we present a novel approach to detect patterns and situations (e.g., playmaking and ball passing of midfielders) based on trajectory data. The application uses the metaphor of a tactic board to offer a graphical query language. With this interactive tactic board, the user can model a game situation or mark a specific situation in the video recording for which all matching occurrences in various games are immediately displayed, and the user can directly jump to the corresponding game scene. Through the additional visualization of key performance indicators (e.g.,the physical load of the players), the user can get a better overall assessment of situations. With the capabilities to find specific game situations and complex patterns in video recordings, the interactive tactic board serves as a useful tool to improve the video analysis process of professional sports teams.},
  language  = {en}
}
@misc{Richly2019,
  author    = {Richly, Keven},
  title     = {A survey on trajectory data management for hybrid transactional and analytical workloads},
  series = {IEEE International Conference on Big Data (Big Data)},
  journal   = {IEEE International Conference on Big Data (Big Data)},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-5386-5035-6},
  issn      = {2639-1589},
  doi       = {10.1109/BigData.2018.8622394},
  pages     = {562 -- 569},
  year      = {2019},
  abstract  = {Rapid advances in location-acquisition technologies have led to large amounts of trajectory data. This data is the foundation for a broad spectrum of services driven and improved by trajectory data mining. However, for hybrid transactional and analytical workloads, the storing and processing of rapidly accumulated trajectory data is a non-trivial task. In this paper, we present a detailed survey about state-of-the-art trajectory data management systems. To determine the relevant aspects and requirements for such systems, we developed a trajectory data mining framework, which summarizes the different steps in the trajectory data mining process. Based on the derived requirements, we analyze different concepts to store, compress, index, and process spatio-temporal data. There are various trajectory management systems, which are optimized for scalability, data footprint reduction, elasticity, or query performance. To get a comprehensive overview, we describe and compare different exciting systems. Additionally, the observed similarities in the general structure of different systems are consolidated in a general blueprint of trajectory management systems.},
  language  = {en}
}
@article{SchlosserRichly2019,
  author    = {Schlosser, Rainer and Richly, Keven},
  title     = {Dynamic pricing under competition with data-driven price anticipations and endogenous reference price effects},
  series = {Journal of revenue and pricing management},
  volume    = {18},
  journal   = {Journal of revenue and pricing management},
  number    = {6},
  publisher = {Palgrave Macmillan},
  address   = {Basingstoke},
  issn      = {1476-6930},
  doi       = {10.1057/s41272-019-00206-5},
  pages     = {451 -- 464},
  year      = {2019},
  abstract  = {Online markets have become highly dynamic and competitive. Many sellers use automated data-driven strategies to estimate demand and to update prices frequently. Further, notification services offered by marketplaces allow to continuously track markets and to react to competitors' price adjustments instantaneously. To derive successful automated repricing strategies is challenging as competitors' strategies are typically not known. In this paper, we analyze automated repricing strategies with data-driven price anticipations under duopoly competition. In addition, we account for reference price effects in demand, which are affected by the price adjustments of both competitors. We show how to derive optimized self-adaptive pricing strategies that anticipate price reactions of the competitor and take the evolution of the reference price into account. We verify that the results of our adaptive learning strategy tend to optimal solutions, which can be derived for scenarios with full information. Finally, we analyze the case in which our learning strategy is played against itself. We find that our self-adaptive strategies can be used to approximate equilibria in mixed strategies.},
  language  = {en}
}