Id |
Subject |
Object |
Predicate |
Lexical cue |
T114 |
0-135 |
Sentence |
denotes |
Aggregating different sensor observations for each room is essential for estimating the room’s COVID-19 risk and cleaning requirements. |
T115 |
136-233 |
Sentence |
denotes |
The visualization of Interior Space Risk State is another task which requires interior modelling. |
T116 |
234-461 |
Sentence |
denotes |
In order to represent the risk of infection and to identify which specific areas of a building require the most cleaning, the status of individual floors should be able to be viewed separately from other floors in the building. |
T117 |
462-648 |
Sentence |
denotes |
The risk of infection for various parts of each floor should be represented in a map with different ways of representing the data that is necessary for determining the risk in each area. |
T118 |
649-868 |
Sentence |
denotes |
Moreover, in order to visualize trajectories for contact tracing and to quickly identify the location of infection spreading behavior within an indoor space, the buildings should be visible as a 3D construct on the map. |
T119 |
869-1223 |
Sentence |
denotes |
Therefore, in order to analyze the IoCT multi-sensors system and visualize it in indoor scenarios, an interoperable 3D building modelling standard, such as the “CityGML” Level of Detail 4 [45], “OGC IndoorGML” [46], building construction standards (e.g., “Building Information Modelling” (BIM), or “Industry Foundation Classes” (IFC) [47]), is necessary. |
T120 |
1224-1308 |
Sentence |
denotes |
The main concern for using those models is their fit and how often they are updated. |
T121 |
1309-1412 |
Sentence |
denotes |
Construction features of indoor spaces are not a major focus of COVID-19 workplace reopening scenarios. |
T122 |
1413-1550 |
Sentence |
denotes |
Instead, the aggregation of sensors in each room, and the connectivity between the rooms, is fundamental for risk assessment and tracing. |
T123 |
1551-1613 |
Sentence |
denotes |
Thus, the OGC IndoorGML is used for the IoCT indoor modelling. |
T124 |
1614-1811 |
Sentence |
denotes |
According to Ryoo et al. [43], the OGC IndoorGML can be used more effectively than CityGML or any other geometric representations of space for analyzing the trajectories of people inside buildings. |
T125 |
1812-1950 |
Sentence |
denotes |
This allows for more accurate appraisal of the types of intersection of trajectories, contact, and exposure for infection risk evaluation. |
T126 |
1951-2205 |
Sentence |
denotes |
Applications such as cleaning risk assessments for COVID-19 workplace reopenings that need to operate efficiently together with indoor scales, various sensors, and objects that are moving and changing over time would benefit from using the OGC IndoorGML. |
T127 |
2206-2364 |
Sentence |
denotes |
3D geometry can be included in an IndoorGML document, and the overlap with other standards (e.g., OGC CityGML) can be addressed by adding external references. |