Routing on Geospatial Reference Information for Transport Networks of Spain

The Spanish National Geographic Institute (IGN) published the first release of the Geographic Reference Information on Transport Networks (GRI-TN) in March 2017. Its main goal was to fulfil INSPIRE Directive requirements, as well as to become the main data source for other products developed by the IGN regarding this theme. During the years following that first release, the focus has been on updating and improving the data. This fact has encouraged new data use cases, which differ from the initially planned ones, have arisen, allowing to detect problems in the data, and highlighting the need to evolve the data model, as well as the way in which they are provided to users (not only formats, but also update frequency). One of those use cases is finding the shortest paths between different points in the area covered by the Road Transport Network. In this article, the methodology used to do it is exposed; likewise, the setbacks that have come up during the process and the current limitations of the GRI-TN datasets in order to get most accurate results.

More Haste, Less Speed: How Update Frequency of Mobile Apps Influences Consumer Interest

The present study proposes a theoretical framework that uncovers the joint effects of the update frequency of apps and product type of the update on consumer interest and its underlying mechanisms. Building on the theory of mental accounting and regulatory focus, we propose that the effects of update frequency on consumer interest are different for hedonic products and utilitarian products. The authors give insights into the main effects with an empirical analysis of a field data set and establish an understanding of the fundamental mechanisms by two laboratory experiments. The findings show that for hedonic products, high update frequency contributes to higher consumer interest by affecting the benefit perception of consumers. For utilitarian products, low update frequency results in higher consumer interest by influencing the risk perception of consumers. Furthermore, the level of update can affect the combined effects of product type and update frequency on consumer interest and, particularly for low update levels, the aforementioned association can be reversed.

Integral-Type Edge-Event- and Edge-Self-Triggered Synchronization to Multi-Agent Systems with Lur’e Nonlinear Dynamics

This paper proposes event- and self-triggered control strategies to achieve distributed synchronization for multiple Lur’e systems with unknown static nonlinearities. Firstly, the integral-type edge-event-triggered function is designed here without Zeno behaviors. Compared to the traditional event-triggered schemes, the considered algorithm has the advantages of reducing controller update frequency and sensor energy consumption. Then, the integral-type self-triggered is further investigated, which implements discontinuous monitoring and discontinuous agent listening. Finally, numerical simulations verified the effectiveness and superiority of our policies.

A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination

Global Navigation Satellite System (GNSS) ultra-rapid orbit is critical for geoscience and real-time engineering applications. To improve the computational efficiency and the accuracy of predicted orbit, a parallel approach for multi-GNSS ultra-rapid orbit determination is proposed based on Message Passing Interface (MPI)/Open Multi Processing (OpenMP). This approach, compared with earlier efficient methods, can improve the efficiency of multi-GNSS ultra-rapid orbit solution without changing the original observation data and retaining the continuity and consistency of the original parameters to be estimated. To obtain high efficiency, three steps are involved in the approach. First and foremost, the normal equation construction is optimized in parallel based on MPI. Second, equivalent reduction of the estimated parameters is optimized using OpenMP parallel method. Third, multithreading is used for parallel orbit extrapolation. Thus, GNSS ultra-rapid orbit determination is comprehensively optimized in parallel, and the computation efficiency is greatly improved. Based on the data from MGEX and IGS stations, experiments are carried out to analyze the performance of the proposed approach in computational efficiency, accuracy and stability. The results show that the approach greatly improves the efficiency of satellite orbit determination. It can realize 1-h update frequency for the multi-GNSS ultra-rapid orbit determination using 88 stations with four-system observations. The accuracy of the GPS, GLONASS, Galileo and BDS ultra-rapid orbit with 1-h update frequency using the parallel approach is approximately 33.4%,31.4%,40.1% and 32.8% higher than that of the original orbit, respectively. The root mean squares (RMS) of GPS, GLONASS, Galileo and BDS predicted orbit are about 3.2 cm, 5.1 cm, 5.6 cm and 11.8 cm. Moreover, the orbit provided by the proposed method has a better stability. The precision loss of all parallel optimization can be negligible and the original correlation between the parameters is fully preserved.

An Empirical Study of the Impact of Data Splitting Decisions on the Performance of AIOps Solutions

AIOps (Artificial Intelligence for IT Operations) leverages machine learning models to help practitioners handle the massive data produced during the operations of large-scale systems. However, due to the nature of the operation data, AIOps modeling faces several data splitting-related challenges, such as imbalanced data, data leakage, and concept drift. In this work, we study the data leakage and concept drift challenges in the context of AIOps and evaluate the impact of different modeling decisions on such challenges. Specifically, we perform a case study on two commonly studied AIOps applications: (1) predicting job failures based on trace data from a large-scale cluster environment and (2) predicting disk failures based on disk monitoring data from a large-scale cloud storage environment. First, we observe that the data leakage issue exists in AIOps solutions. Using a time-based splitting of training and validation datasets can significantly reduce such data leakage, making it more appropriate than using a random splitting in the AIOps context. Second, we show that AIOps solutions suffer from concept drift. Periodically updating AIOps models can help mitigate the impact of such concept drift, while the performance benefit and the modeling cost of increasing the update frequency depend largely on the application data and the used models. Our findings encourage future studies and practices on developing AIOps solutions to pay attention to their data-splitting decisions to handle the data leakage and concept drift challenges.

Distributed Attitude Synchronization for Spacecraft Formation Flying via Event-Triggered Control

The distributed attitude synchronization control problem for spacecraft formation flying subject to limited energy and computational resources is addressed based on event-triggered mechanism. Firstly, a distributed event-driven controller is designed to achieve attitude coordination with the limitation of energy and computing resources. Under the proposed control strategy, the controller is only updated at the event triggering instants, which effectively reduces the update frequency. Subsequently, an event-triggered strategy is developed to further decrease energy consumption and the amount of computation. The proposed event-triggered function only requires the latest state information about its neighbors, implying that the trigger threshold does not need to be calculated continuously. It is shown that the triggering interval between two successive events is strictly positive, showing that the control system has no Zeno phenomenon. Moreover, the update frequency of the proposed controller can be reduced by more than 90% compared to the update frequency of the corresponding time-driven controller with an update frequency of 10 Hz by choosing appropriate control parameters and the control system can still achieve high-precision convergence. Finally, the effectiveness of the constructed control scheme is verified by numerical simulations.

Sales and operations planning for new products: a parallel process?

PurposeThis paper aims to detail how a Sales and Operations Planning (S&OP) process can be designed to support the planning requirements of recently introduced products.Design/methodology/approachDesign science research was conducted to propose and implement an S&OP model for demand fulfillment after the introduction of new products. The results were analyzed using the CIMO (Context, Intervention, Mechanisms and Outcomes) logic, and two sets of design propositions were formulated.FindingsAn S&OP process for new products can reduce additional costs for market fulfillment by concentrating the planning efforts on new products, aligning organizational efforts, and increasing the sales and supply chain information’s update frequency.Research limitations/implicationsThe outcomes of S&OP new products were analyzed in a single organization and are limited to the contextual factors presented.Practical implicationsThis paper describes in detail how to organize an S&OP focused on new products. By considering the contextual factors and design propositions, managers can potentially increase the success of new products introduction (NPI) in their context.Originality/valueA specific S&OP process focused on new products is a viable solution and could co-exist with a traditional S&OP process. Moreover, we identified six contextual factors that influence the outcomes of the S&OP new products.

Comparative verification in complex topography

<p>Verification is a core activity in weather forecasting. Insights from verification are used for monitoring, for reporting, to support and motivate development of the forecasting system, and to allow users to maximize forecast value. Due to the broad range of applications for which verification provides valuable input, the range of questions one would like to answer can be very large. Static analyses and summary verification results are often insufficient to cover this broad range. To this end, we developed an interactive verification platform at MeteoSwiss that allows users to inspect verification results from a wide range of angles to find answers to their specific questions.</p><p>We present the technical setup to achieve a flexible yet performant interactive platform and two prototype applications: monitoring of direct model output from operational NWP systems and understanding of the capabilities and limitations of our pre-operational postprocessing. We present two innovations that illustrate the user-oriented approach to comparative verification adopted as part of the platform. To facilitate the comparison of a broad range of forecasts issued with varying update frequency, we rely on the concept of time of verification to collocate the most recent available forecasts at the time of day at which the forecasts are used. In addition, we offer a matrix selection to more flexibly select forecast sources and scores for comparison. Doing so, we can for example compare the mean absolute error (MAE) for deterministic forecasts to the MAE and continuous ranked probability scores of probabilistic forecasts to illustrate the benefit of using probabilistic forecasts.</p>