TPrediction1 CC-90011 supplier entity for notifying the prediction values to Draco for storing the historic context plus the internet application to present the prediction worth to the user. A graphical representation on the element that interacts together with the entities, subscriptions, and also the structure of how the two entities with all of their attributes are modeled is presented in Figure 11.Momelotinib web Sensors 2021, 21,25 ofFigure 11. Schema of entities and service subscriptions.six.two.six. Workflow The workflow from the technique is presented in Figure 12 and is described as follows: 1. 2. 3. four. 5. six. 7. 8. The user sends a prediction request towards the web application (WebApp). The WebApp generates a brand new prediction request for the entity. ReqTicketPrediction1 designed in Orion. Orion updates the ReqTicketPrediction1 entity together with the new values and sends a notification with this update for the running SparkJob. SparkJob makes use of the trained model to compute the prediction based on the values received in the notification. When the prediction is generated, SparkJob sends a prediction response for the ResTicketPrediction1 entity stored in Orion. Orion updates the ResTicketPrediction1 entity and generates two notifications: 1 for the WebApp and the other for Draco. Once the ResTicketPrediction1 notification is received by the WebApp, it shows the prediction for the user. Lastly, when Draco receives the notification about the update from the ReqsTicketPrediction1, it requires the values and persists the historic context data into MongoDB.Sensors 2021, 21,26 ofFigure 12. Buy prediction systems workflow.7. Conclusions and Future Function This article describes a reference implementation for giving data analytics capabilities to context-aware smart environments. The underlying architecture is divided into four layers, informed by available literature within the field: physical, middleware, application, and safety. As such, it considers the comprehensive data lifecycle, from information acquisition by means of sensors and also other IoT devices, to data processing using Significant Data technologies and presentation for the finish user. Our implementation relies on FIWARE GEs and generally made use of open supply technologies, a combination which has established useful in the past for constructing other types of wise options such as digital twins [46], data usage controlled sharing environments [47,48], and enhanced authentication systems [49]. Within this article, we show how, by combining these developing blocks, we provide a robust, versatile, scalable, and safe approach to deliver context-aware data analytics in clever environments. Moreover, we reap the benefits of the existing Smart Data Models based on the NGSI normal to conveniently adapt our reference implementation to various domains. We supply two example use circumstances to validate the generalizability of our proposal: one in Clever Farming as well as the other in Wise Industry (retail). These two settings highlight how our reference implementation supports data from quite a few unique sources, too as a number of operations including complex event processing and machine learning. Our hope is the fact that our open-source reference implementation, along with the step-by-step descriptionSensors 2021, 21,27 ofof our example use instances, presents some clues to researchers, developers, and practitioners on ways to operationalize their own context-aware smart environments based on FIWARE. Additional study is needed to establish how the reference implementation presented within this work is usually combined with emerging paradigm.