# Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture
## 6 Performance Evaluation  
Performance characteristics have been obtained by using the Oracle 19c database system based on the relational platform. For the evaluation, a table monitoring 100 drivers was used, each row was delimited by the composite primary key--identification of the driver (driver_id) and spatio-temporal spheres (location_def, time_def). In the first phase, just data based on the smartphone sensors were used (5 sensors we used monitoring speed of the vehicle, eye-blinking, concentration, noise, and the number of people in the car). Data (5 sensors (s)) were obtained three times per minute (f) for 100 drivers (d) during three hours a day (t) (covering the transport to the office and back + going to the standard daily shops) monitored during one month--twenty working days (m). Thus, the total data amount in the table was (value of the t is expressed in minutes):  
\[\texttt{s}*\texttt{f}*\texttt{d}*\texttt{t}*\texttt{m}=5*3*100*180*20=5,\!400,\!000 \text{ data rows.}\]  
The total amount of the data in the table was 5.4 million. The main index was defined just for the driver and time-positional data definition, 10% of rows were delimited by at least partial undefinition (three categories were used for the undefinition). No other indexes were developed, therefore this main index was used for the Flower index approach as the Master index. Experiment results were provided using Oracle Database 19c Enterprise Edition Release 19.3.0.0.0--64 bit Production. Parameters of the used computer are:  
* Processor: Intel Xeon E5620; 2.4 GHz (8 cores),
* Operation memory: 48 GB DDR 1333 MHz
* Disk storage capacity: 1000 GB (SSD). To evaluate the performance, two approaches were used as the referencing. **Model TAF** does not deal with undefined values in the index definition, thus sequential scanning is necessary to be executed during the data retrieval. The second referential model can be named **Model DEF**, by which undefined values are transformed into the default values in the preprocessing layer outside the database. Thus, it is always ensured, that real data are present in the database, undefined values are modeled by using default value transformation. As evident from the results, it has additional demands on the disc storage. Data evaluation is more complicated, as well, whereas the default value has to be replaced during the result set composition and transport to the client. A computational study dealing with the performance reflects processing costs delimited by the time demand aspect. **Experiment 1--temporal architecture**  
As stated, the environment deals with data 5 sensors providing data for 100 drivers during three hours. In this evaluation experiment, 2 sensors were time-synchronized. In case of any Update operation, the random number of sensor values is changed, ranging from 2 to 4. This part deals with the temporal architecture with an emphasis on the processed granularity. Model **Model_obj** is delimited by the object architecture capturing the whole sensor image occurring during any Update statement. **Model_column** manages just separate sensors regardless of the synchronization of the 2 sensors. As evident, in this case, each changed attribute forces the system to load one extra row into the temporal layer. **Model_group** benefits from the synchronization group, which is defined and maintained automatically, based on the structure of the changes. If the group is changed, instead of managing individual sensors separately, data_val definition is used consisting of 2 physical sensor data, however, only one row for the whole group was inserted into the main temporal level. As evident, it proposes significant benefit during the Insert or Update statements, as well as during the data retrieval, whereas the result set should contain only actually changed values. Finally, model **Model_group_ext** is evaluated, which is based on synchronization based on the temporal data, as well as the positional data. In our case, we divide GPS data into 8 regions characterizing the map of our country.