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The Internet of things has produced several heterogeneous devices and data models for sensors/actuators, physical and virtual. Corresponding data must be aggregated and their models have to be put in relationships with the general knowledge to make them immediately usable by visual analytics tools, APIs, and other devices. In this paper, models and tools for data ingestion and regularization are presented to simplify and enable the automated visual representation of corresponding data. The addressed problems are related to the (i) regularization of the high heterogeneity of data that are available in the IoT devices (physical or virtual) and KPIs (key performance indicators), thus allowing such data in elements of hypercubes to be reported, and (ii) the possibility of providing final users with an index on views and data structures that can be directly exploited by graphical widgets of visual analytics tools, according to different operators. The solution analyzes the loaded data to extract and generate the IoT device model, as well as to create the instances of the device and generate eventual time series. The whole process allows data for visual analytics and dashboarding to be prepared in a few clicks. The proposed IoT device model is compliant with FIWARE NGSI and is supported by a formal definition of data characterization in terms of value type, value unit, and data type. The resulting data model has been enforced into the Snap4City dashboard wizard and tool, which is a GDPR-compliant multitenant architecture. The solution has been developed and validated by considering six different pilots in Europe for collecting big data to monitor and reason people flows and tourism with the aim of improving quality of service; it has been developed in the context of the HERIT-DATA Interreg project and on top of Snap4City infrastructure and tools. The model turned out to be capable of meeting all the requirements of HERIT-DATA, while some of the visual representation tools still need to be updated and furtherly developed to add a few features.

We evaluated the costs and clinical outcomes of episodes of suspected sepsis in hematological patients. A propensity score-matched study was planned, comparing a retrospective cohort managed with standard assays and a prospective cohort managed with the addition of a molecular assay. Diagnostic procedures and therapy were considered as costs variables. The primary clinical endpoint was sepsis-related mortality, whereas the length of each suspected sepsis episode was investigated as a secondary endpoint. A total of 137 and 138 episodes in the prospective and the retrospective cohorts were studied, respectively; 101 pairs of highly matched episodes were analyzed, evidencing a trend of higher mortality in the retrospective cohort. No difference in length of suspected sepsis episode was observed. Significant savings were observed in the prospective cohort, especially due to reduced costs in antifungal therapy. The apparently more expensive molecular assay favored a more rational use of economic resources without influencing, and probably improving, the clinical outcome.