Suchergebnisse

Die Integration heterogener Messsysteme in eine Produktions-IT führt zu einem hohem Aufwand, da die Schnittstelle des Gerätes manuell an das verwendete Protokoll und Datenformat angepasst werden muss. In diesem Beitrag wird vorgestellt, wie durch die Verwendung einer domänenspezifischen Beschreibungssprache die Schnittstelle eines Messsystems, prototypisch basierend auf HTTP und MQTT, auf Basis eines Meta-Datenmodells generiert und sich Informationen mit Kontext- und Metadaten annotieren lassen.

The integration of heterogeneous measurement systems into a production IT leads to high effort, as the interface of the device must be adapted manually to the protocol and data format used. By using a domain-specific description language, this article presents how the interface of a measurement system, prototypically based on HTTP and MQTT, can be generated based on a meta-data-model and how measurement data can be annotated with context- and metadata.

Attributing foodborne illnesses to food sources is essential to conceive, prioritize, and assess the impact of public health policy measures. The Bayesian microbial subtyping attribution model by Hald et al. is one of the most advanced approaches to attribute sporadic cases; it namely allows taking into account the level of exposure to the sources and the differences between bacterial types and between sources. This step forward requires introducing type and source‐dependent parameters, and generates overparameterization, which was addressed in Hald's paper by setting some parameters to constant values. We question the impact of the choices made for the parameterization (parameters set and values used) on model robustness and propose an alternative parameterization for the Hald model. We illustrate this analysis with the 2005 French data set of non‐typhi Salmonella. Mullner's modified Hald model and a simple deterministic model were used to compare the results and assess the accuracy of the estimates. Setting the parameters for bacterial types specific to a unique source instead of the most frequent one and using data‐based values instead of arbitrary values enhanced the convergence and adequacy of the estimates and led to attribution estimates consistent with the other models' results. The type and source parameters estimates were also coherent with Mullner's model estimates. The model appeared to be highly sensitive to parameterization. The proposed solution based on specific types and data‐based values improved the robustness of estimates and enabled the use of this highly valuable tool successfully with the French data set.