# QB4 MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web
## 3 Research Methods
### Mobility Data Type  
This research explores a new vocabulary to represent MOs on the SW for mobility analysis related to the MO data type for the purpose of query retrieval. The MO data have a data type that consists of spatial and temporal attributes. Spatial attributes describe real-world phenomena consisting of descriptive components presented with traditional data types such as integers, booleans, strings, and dates. Spatial attributes enriched with spatial extent components explain the real world in geometric data types such as Point, Multi Points, Line, Polygon, and Surface (MADS Model). Spatial attributes are stored differently and are often called thematic layers (themes). Temporal attributes describe time and changes in time that could be defined with time types and temporal types. [PERSON] et al. define temporal types as moving types [49]. Time types represent the time, for instance, timestamp, timestamp with time zone, period, timestamp set, and period set. Temporal types are based on time types. Temporal types include temporal integer, temporal float, temporal points, and temporal geometries. The temporal integer type could represent the evolution of employee salary. Temporal points denote the flight path of an aircraft or tourist. An example of a temporal float is the temperature of the human body. The combination of spatial and temporal types creates new data types called temporal-spatial types [50]. This combination occurs in moving objects, such as pedestrians, trucks, moving clouds, and shrinking agricultural areas that are mostly represented using the point and polygon. Figure 1 shows an example of moving trucks in our use case of an oil palm plantation. These trucks deliver Oil Palm Fruit Fresh Bunches (FFB) from plantations to the Oil Palm Mill (OPM). Figure 1: Examples of temporal-spatial types of data used in this research: the delivery path of the trucks is shown in colored line. Table 1 shows a sample of mobility data that have temporal-spatial types. The data contain the time, the locations of the trucks in longitude and latitude, and the float types of the truckload values. Our mobile apps captured this data to monitor the truckload and trajectory in the Oil Palm Fruit Plantation [51]. The temporal-spatial types could be extended to represent a field/region that varies in time and space. The spatiotemporal database is then used to store spatial types, temporal types, and temporal-spatial types. This database could store the position of moving objects at any point in time, changing regions continuously, and so on. Although some examples of queries could be solved, such as _\"When the last truck starting from Alfa Block arrive at Palm Oil Mills?\", \"How fast the last truck arrived at Palm Oil Mills this morning shift?\"_, this kind of database does not provide a foundation for such complex analytical queries as _\"What is the average truckload passing the #27 road segment on the Alfa block in the X Company's oil palm plantations this year?\"_ or _\"What is the total weight of oil palm production carried by company trucks in each plantation block in the third quarter of 2020?\"_. These kinds of queries could be handled efficiently using Mobility DW with mobility operation capabilities. The Mobility DW has extended support for the concept of spatiotemporal data warehouses that contain spatial types, temporal types, and temporal-spatial types of data. Implementation of the temporal types could be found on MobilityDB [52], as a data type tool, tint, tfloat, text, based on the primitive data types boolean, integer, real and text, respectively. MobilityDB also has temporal-spatial types such as tgeompoint and tgeogpoint, which are based on spatial type points [53]. Because they allow the representation of values that change over time, temporal types are useful in mobility domain applications. Temporal types provide a more natural and simple representation of time-varying value.