TY - GEN A1 - Halfpap, Stefan A1 - Schlosser, Rainer T1 - A Comparison of Allocation Algorithms for Partially Replicated Databases T2 - 2019 IEEE 35th International Conference on Data Engineering (ICDE) N2 - Increasing demand for analytical processing capabilities can be managed by replication approaches. However, to evenly balance the replicas' workload shares while at the same time minimizing the data replication factor is a highly challenging allocation problem. As optimal solutions are only applicable for small problem instances, effective heuristics are indispensable. In this paper, we test and compare state-of-the-art allocation algorithms for partial replication. By visualizing and exploring their (heuristic) solutions for different benchmark workloads, we are able to derive structural insights and to detect an algorithm's strengths as well as its potential for improvement. Further, our application enables end-to-end evaluations of different allocations to verify their theoretical performance. Y1 - 2019 SN - 978-1-5386-7474-1 SN - 978-1-5386-7475-8 U6 - https://doi.org/10.1109/ICDE.2019.00226 SN - 1084-4627 SN - 2375-026X SN - 1063-6382 SP - 2008 EP - 2011 PB - IEEE CY - New York ER - TY - JOUR A1 - Schlosser, Rainer A1 - Walther, Carsten A1 - Boissier, Martin A1 - Uflacker, Matthias T1 - Automated repricing and ordering strategies in competitive markets JF - AI communications : AICOM ; the European journal on artificial intelligence N2 - Merchants on modern e-commerce platforms face a highly competitive environment. They compete against each other using automated dynamic pricing and ordering strategies. Successfully managing both inventory levels as well as offer prices is a challenging task as (i) demand is uncertain, (ii) competitors strategically interact, and (iii) optimized pricing and ordering decisions are mutually dependent. We show how to derive optimized data-driven pricing and ordering strategies which are based on demand learning techniques and efficient dynamic optimization models. We verify the superior performance of our self-adaptive strategies by comparing them to different rule-based as well as data-driven strategies in duopoly and oligopoly settings. Further, to study and to optimize joint dynamic ordering and pricing strategies on online marketplaces, we built an interactive simulation platform. To be both flexible and scalable, the platform has a microservice-based architecture and allows handling dozens of competing merchants and streams of consumers with configurable characteristics. KW - Dynamic pricing KW - inventory management KW - demand learning KW - oligopoly competition KW - e-commerce Y1 - 2019 U6 - https://doi.org/10.3233/AIC-180603 SN - 0921-7126 SN - 1875-8452 VL - 32 IS - 1 SP - 15 EP - 29 PB - IOS Press CY - Amsterdam 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 - Schlosser, Rainer A1 - Chenavaz, Régis Y. A1 - Dimitrov, Stanko T1 - Circular economy BT - joint dynamic pricing and recycling investments JF - International journal of production economics N2 - In a circular economy, the use of recycled resources in production is a key performance indicator for management. Yet, academic studies are still unable to inform managers on appropriate recycling and pricing policies. We develop an optimal control model integrating a firm's recycling rate, which can use both virgin and recycled resources in the production process. Our model accounts for recycling influence both at the supply- and demandsides. The positive effect of a firm's use of recycled resources diminishes over time but may increase through investments. Using general formulations for demand and cost, we analytically examine joint dynamic pricing and recycling investment policies in order to determine their optimal interplay over time. We provide numerical experiments to assess the existence of a steady-state and to calculate sensitivity analyses with respect to various model parameters. The analysis shows how to dynamically adapt jointly optimized controls to reach sustainability in the production process. Our results pave the way to sounder sustainable practices for firms operating within a circular economy. KW - Dynamic pricing KW - Recycling investments KW - Optimal control KW - General demand function KW - Circular economy Y1 - 2021 U6 - https://doi.org/10.1016/j.ijpe.2021.108117 SN - 0925-5273 SN - 1873-7579 VL - 236 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schlosser, Rainer A1 - Boissier, Martin T1 - Dealing with the dimensionality curse in dynamic pricing competition BT - Using frequent repricing to compensate imperfect market anticipations JF - Computers & Operations Research N2 - Most sales applications are characterized by competition and limited demand information. For successful pricing strategies, frequent price adjustments as well as anticipation of market dynamics are crucial. Both effects are challenging as competitive markets are complex and computations of optimized pricing adjustments can be time-consuming. We analyze stochastic dynamic pricing models under oligopoly competition for the sale of perishable goods. To circumvent the curse of dimensionality, we propose a heuristic approach to efficiently compute price adjustments. To demonstrate our strategy’s applicability even if the number of competitors is large and their strategies are unknown, we consider different competitive settings in which competitors frequently and strategically adjust their prices. For all settings, we verify that our heuristic strategy yields promising results. We compare the performance of our heuristic against upper bounds, which are obtained by optimal strategies that take advantage of perfect price anticipations. We find that price adjustment frequencies can have a larger impact on expected profits than price anticipations. Finally, our approach has been applied on Amazon for the sale of used books. We have used a seller’s historical market data to calibrate our model. Sales results show that our data-driven strategy outperforms the rule-based strategy of an experienced seller by a profit increase of more than 20%. KW - Dynamic pricing KW - Oligopoly competition KW - Dynamic programming KW - Data-driven strategies KW - E-commerce Y1 - 2018 U6 - https://doi.org/10.1016/j.cor.2018.07.011 SN - 0305-0548 SN - 1873-765X VL - 100 SP - 26 EP - 42 PB - Elsevier CY - Oxford ER - 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 - GEN A1 - Schlosser, Rainer A1 - Kossmann, Jan A1 - Boissier, Martin T1 - Efficient Scalable Multi-Attribute Index Selection Using Recursive Strategies T2 - 2019 IEEE 35th International Conference on Data Engineering (ICDE) N2 - An efficient selection of indexes is indispensable for database performance. For large problem instances with hundreds of tables, existing approaches are not suitable: They either exhibit prohibitive runtimes or yield far from optimal index configurations by strongly limiting the set of index candidates or not handling index interaction explicitly. We introduce a novel recursive strategy that does not exclude index candidates in advance and effectively accounts for index interaction. Using large real-world workloads, we demonstrate the applicability of our approach. Further, we evaluate our solution end to end with a commercial database system using a reproducible setup. We show that our solutions are near-optimal for small index selection problems. For larger problems, our strategy outperforms state-of-the-art approaches in both scalability and solution quality. Y1 - 2019 SN - 978-1-5386-7474-1 U6 - https://doi.org/10.1109/ICDE.2019.00113 SN - 1084-4627 SP - 1238 EP - 1249 PB - IEEE 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 - JOUR A1 - Schlosser, Rainer T1 - Stochastic dynamic pricing and advertising in isoelastic oligopoly models JF - European Journal of Operational Research N2 - In this paper, we analyze stochastic dynamic pricing and advertising differential games in special oligopoly markets with constant price and advertising elasticity. We consider the sale of perishable as well as durable goods and include adoption effects in the demand. Based on a unique stochastic feedback Nash equilibrium, we derive closed-form solution formulas of the value functions and the optimal feedback policies of all competing firms. Efficient simulation techniques are used to evaluate optimally controlled sales processes over time. This way, the evolution of optimal controls as well as the firms’ profit distributions are analyzed. Moreover, we are able to compare feedback solutions of the stochastic model with its deterministic counterpart. We show that the market power of the competing firms is exactly the same as in the deterministic version of the model. Further, we discover two fundamental effects that determine the relation between both models. First, the volatility in demand results in a decline of expected profits compared to the deterministic model. Second, we find that saturation effects in demand have an opposite character. We show that the second effect can be strong enough to either exactly balance or even overcompensate the first one. As a result we are able to identify cases in which feedback solutions of the deterministic model provide useful approximations of solutions of the stochastic model. KW - Pricing KW - Advertising KW - Stochastic differential games KW - Oligopoly competition KW - Adoption effects Y1 - 2017 U6 - https://doi.org/10.1016/j.ejor.2016.11.021 SN - 0377-2217 SN - 1872-6860 VL - 259 SP - 1144 EP - 1155 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Hagedorn, Christopher A1 - Huegle, Johannes A1 - Schlosser, Rainer T1 - Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning JF - Journal of intelligent manufacturing N2 - In discrete manufacturing, the knowledge about causal relationships makes it possible to avoid unforeseen production downtimes by identifying their root causes. Learning causal structures from real-world settings remains challenging due to high-dimensional data, a mix of discrete and continuous variables, and requirements for preprocessing log data under the causal perspective. In our work, we address these challenges proposing a process for causal reasoning based on raw machine log data from production monitoring. Within this process, we define a set of transformation rules to extract independent and identically distributed observations. Further, we incorporate a variable selection step to handle high-dimensionality and a discretization step to include continuous variables. We enrich a commonly used causal structure learning algorithm with domain-related orientation rules, which provides a basis for causal reasoning. We demonstrate the process on a real-world dataset from a globally operating precision mechanical engineering company. The dataset contains over 40 million log data entries from production monitoring of a single machine. In this context, we determine the causal structures embedded in operational processes. Further, we examine causal effects to support machine operators in avoiding unforeseen production stops, i.e., by detaining machine operators from drawing false conclusions on impacting factors of unforeseen production stops based on experience. KW - Causal structure learning KW - Log data KW - Causal inference KW - Manufacturing KW - industry Y1 - 2022 U6 - https://doi.org/10.1007/s10845-022-01952-x SN - 0956-5515 SN - 1572-8145 VL - 33 IS - 7 SP - 2027 EP - 2043 PB - Springer CY - Dordrecht ER - TY - GEN A1 - Halfpap, Stefan A1 - Schlosser, Rainer T1 - Workload-Driven Fragment Allocation for Partially Replicated Databases Using Linear Programming T2 - 2019 IEEE 35th International Conference on Data Engineering (ICDE) N2 - In replication schemes, replica nodes can process read-only queries on snapshots of the master node without violating transactional consistency. By analyzing the workload, we can identify query access patterns and replicate data depending to its access frequency. In this paper, we define a linear programming (LP) model to calculate the set of partial replicas with the lowest overall memory capacity while evenly balancing the query load. Furthermore, we propose a scalable decomposition heuristic to calculate solutions for larger problem sizes. While guaranteeing the same performance as state-of-the-art heuristics, our decomposition approach calculates allocations with up to 23% lower memory footprint for the TPC-H benchmark. KW - database replication KW - allocation problem KW - linear programming Y1 - 2019 SN - 978-1-5386-7474-1 SN - 978-1-5386-7475-8 U6 - https://doi.org/10.1109/ICDE.2019.00188 SN - 1084-4627 SN - 2375-026X SN - 1063-6382 SP - 1746 EP - 1749 PB - IEEE CY - New York ER -