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ItemEvaluation and Optimal Calibration of Purchase Time Recommendation Services( 2019-01-08)Price Comparison Sites enable customers to make better – more informed, less costly – buying decisions through providing price information and offering buying advice in the form of prediction services. While these services differ to some extent, they are comparable regarding their prediction target and usually monitor every arbitrarily small price decrease. We use a large data set of daily minimum prices for 272 smartphones consisting of 198,560 daily price movements from a Price Comparison Site to show that the standard prediction setting is not optimal. A custom evaluation framework allows the maximization of the achievable savings by altering the calibration of the forecasting service to monitor changes that exceed a certain threshold. Additionally, we show that time series features calculated in a calibration period can be used to obtain precise out of sample estimates of the saving optimal forecasting setting.
ItemStatistical Analysis and Modeling of Heterogeneous Workloads on Amazon's Public Cloud Infrastructure( 2019-01-08)Workload modeling in public cloud environments is challenging due to reasons such as infrastructure abstraction, workload heterogeneity and a lack of defined metrics for performance modeling. This paper presents an approach that applies statistical methods for distribution analysis, parameter estimation and Goodness-of-Fit (GoF) tests to develop theoretical (estimated) models of heterogeneous workloads on Amazon's public cloud infrastructure using compute, memory and IO resource utilization data.
ItemForecasting the Demand for Emergency Medical Services( 2019-01-08)Accurate forecast of the demand for emergency medical services (EMS) can help in providing quick and efficient medical treatment and transportation of out-of-hospital patients. The aim of this research was to develop a forecasting model and investigate which factors are relevant to include in such model. The primary data used in this study was information about ambulance calls in three Swedish counties during the years 2013 and 2014. This information was processed, assigned to spatial grid zones and complemented with population and zone characteristics. A Zero-Inflated Poisson (ZIP) regression approach was then used to select significant factors and develop the forecasting model. The model was compared to the forecasting model that is currently incorporated in the EMS information system used by the ambulance dispatchers. The results show that the proposed model performs better than the existing one.
ItemIntroduction to the Minitrack on Service Analytics( 2019-01-08)