Use Case Scenario Sample Clauses

Use Case Scenario. The DVCL with CA system allows that remote students can work together. While the students will use their own computer, the university has to provide the CA. In our use case scenario we assume that a student uses the DVCL client outside the university (e.g. at home) utilising an internet connection to connect to the CA. A mobile environment using a cellular data plan is also possible. Figure 19 shows a corresponding use case diagram of the DVCL with CA. The left side represents the student’s home, connected via internet (cloud in the middle) to the university’s CA on the right side. A student, as a user of his desktop or laptop computer, uses the DVCL client to interact with a university’s CA using a control channel connection. An additional data channel connection will be established for each remotely connected virtual network. Home University Desktop Server Internet User Central Authority (Session) Data Channel Control Channel Central Authority

Use Case Scenario. One of the key issues in heterogeneous ecosystem of smart cities is real-time traffic data analysis. Enabling smart cities to efficiently manage traffic data and provide alternative routes will not only help in reducing transportation cost but also pollution that has been caused by traffic congestion. As a use case scenario, we use public traffic data17 that has been obtained from the city of Aarhus in Denmark. The database consists of traffic data that has been measured among various sensors in different cities providing information regarding the geographical location, timestamp, and traffic intensity such as average speed and vehicle count. The data is taken from 135 sensors and samples every 5 minutes. While most of the systems constantly create and transmit raw sensor data, we need to be able to express a narrower, more specific workflow such as representation of aggregated and summarised data for individual sensor recordings and the smart city workflow. This will pave the way towards having scalable systems, and reduce memory and computational cost of massive amount of real-time data produced by sensors. For this reason, the semantic representation of the summarised data is as important as annotation of raw stream data. In this Section, we exemplify the use of the proposed information model, describing the outcome of a pair of sensor recordings, and its representation in a road traffic environment. 17 xxxx://xxx.xxxx.xx/dataset/realtids-trafikdata Figure 11 illustrates a sample location from city of Aarhus in Denmark on Google Maps showing a pair of traffic sensor points, that have been virtualised in our system, and aggregated and semantically annotated based on Stream Annotation Ontology. The sensor points refer to exact geographical coordinates (i.e. latitude, longitude), and linked to resources such as DBPedia18 and GeoNames19 that are publicly available as a part of the Linked Open Data cloud. In aggregation process, the streaming data that has been obtained from these sensors, divided into segments and patterns is created for each segment. These patterns represent an aggregation of a set of raw sensor data during a period of time. The pattern construction is performed using the Symbolic Aggregate Approximation (SAX) technique (Xxxxxxx Xxx, 2003). SAX is used in data mining and time series data for dimensionality reduction and creation of symbolic patterns. It divides a time series data into equal segments and then creates a string representation...