TY - JOUR A1 - Schlosser, Rainer A1 - Richly, Keven T1 - Dynamic pricing under competition with data-driven price anticipations and endogenous reference price effects JF - Journal of revenue and pricing management N2 - 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. KW - Dynamic pricing competition KW - Data-driven price anticipation KW - e-Commerce KW - Dynamic programming KW - Response strategies Y1 - 2019 U6 - https://doi.org/10.1057/s41272-019-00206-5 SN - 1476-6930 SN - 1477-657X VL - 18 IS - 6 SP - 451 EP - 464 PB - Palgrave Macmillan CY - Basingstoke ER - TY - JOUR A1 - Richly, Keven A1 - Schlosser, Rainer A1 - Boissier, Martin T1 - Budget-conscious fine-grained configuration optimization for spatio-temporal applications JF - Proceedings of the VLDB Endowment N2 - Based on the performance requirements of modern spatio-temporal data mining applications, in-memory database systems are often used to store and process the data. To efficiently utilize the scarce DRAM capacities, modern database systems support various tuning possibilities to reduce the memory footprint (e.g., data compression) or increase performance (e.g., additional indexes). However, the selection of cost and performance balancing configurations is challenging due to the vast number of possible setups consisting of mutually dependent individual decisions. In this paper, we introduce a novel approach to jointly optimize the compression, sorting, indexing, and tiering configuration for spatio-temporal workloads. Further, we consider horizontal data partitioning, which enables the independent application of different tuning options on a fine-grained level. We propose different linear programming (LP) models addressing cost dependencies at different levels of accuracy to compute optimized tuning configurations for a given workload and memory budgets. To yield maintainable and robust configurations, we extend our LP-based approach to incorporate reconfiguration costs as well as a worst-case optimization for potential workload scenarios. Further, we demonstrate on a real-world dataset that our models allow to significantly reduce the memory footprint with equal performance or increase the performance with equal memory size compared to existing tuning heuristics. KW - General Earth and Planetary Sciences KW - Water Science and Technology KW - Geography, Planning and Development Y1 - 2022 U6 - https://doi.org/10.14778/3565838.3565858 SN - 2150-8097 VL - 15 IS - 13 SP - 4079 EP - 4092 PB - Association for Computing Machinery (ACM) CY - [New York] ER - TY - JOUR A1 - Richly, Keven A1 - Brauer, Janos A1 - Schlosser, Rainer T1 - Predicting location probabilities of drivers to improved dispatch decisions of transportation network companies based on trajectory data JF - Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - ICORES N2 - The demand for peer-to-peer ridesharing services increased over the last years rapidly. To cost-efficiently dispatch orders and communicate accurate pick-up times is challenging as the current location of each available driver is not exactly known since observed locations can be outdated for several seconds. The developed trajectory visualization tool enables transportation network companies to analyze dispatch processes and determine the causes of unexpected delays. As dispatching algorithms are based on the accuracy of arrival time predictions, we account for factors like noise, sample rate, technical and economic limitations as well as the duration of the entire process as they have an impact on the accuracy of spatio-temporal data. To improve dispatching strategies, we propose a prediction approach that provides a probability distribution for a driver’s future locations based on patterns observed in past trajectories. We demonstrate the capabilities of our prediction results to ( i) avoid critical delays, (ii) to estimate waiting times with higher confidence, and (iii) to enable risk considerations in dispatching strategies. KW - trajectory data KW - location prediction algorithm KW - Peer-to-Peer ridesharing KW - transport network companies KW - risk-aware dispatching Y1 - 2020 PB - Springer CY - Berlin ER - TY - GEN A1 - Richly, Keven A1 - Brauer, Janos A1 - Schlosser, Rainer T1 - Predicting location probabilities of drivers to improved dispatch decisions of transportation network companies based on trajectory data T2 - Postprints der Universität Potsdam : Reihe der Digital Engineering Fakultät N2 - The demand for peer-to-peer ridesharing services increased over the last years rapidly. To cost-efficiently dispatch orders and communicate accurate pick-up times is challenging as the current location of each available driver is not exactly known since observed locations can be outdated for several seconds. The developed trajectory visualization tool enables transportation network companies to analyze dispatch processes and determine the causes of unexpected delays. As dispatching algorithms are based on the accuracy of arrival time predictions, we account for factors like noise, sample rate, technical and economic limitations as well as the duration of the entire process as they have an impact on the accuracy of spatio-temporal data. To improve dispatching strategies, we propose a prediction approach that provides a probability distribution for a driver’s future locations based on patterns observed in past trajectories. We demonstrate the capabilities of our prediction results to ( i) avoid critical delays, (ii) to estimate waiting times with higher confidence, and (iii) to enable risk considerations in dispatching strategies. T3 - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 9 KW - trajectory data KW - location prediction algorithm KW - Peer-to-Peer ridesharing KW - transport network companies KW - risk-aware dispatching Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-524040 IS - 9 ER - TY - GEN A1 - Richly, Keven T1 - A survey on trajectory data management for hybrid transactional and analytical workloads T2 - IEEE International Conference on Big Data (Big Data) N2 - 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. KW - Trajectory Data Management KW - Spatio-Temporal Data KW - Survey Y1 - 2019 SN - 978-1-5386-5035-6 U6 - https://doi.org/10.1109/BigData.2018.8622394 SN - 2639-1589 SP - 562 EP - 569 PB - IEEE CY - New York ER - TY - GEN A1 - Richly, Keven T1 - Leveraging spatio-temporal soccer data to define a graphical query language for game recordings T2 - IEEE International Conference on Big Data (Big Data) N2 - 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. KW - Spatio-temporal data analysis KW - soccer analytics KW - graphical query language Y1 - 2019 SN - 978-1-5386-5035-6 U6 - https://doi.org/10.1109/BigData.2018.8622159 SN - 2639-1589 SP - 3456 EP - 3463 PB - IEEE CY - New York ER -