## 2 Related Works
### [PERSON]'s Analytic Ascendancy Model  
As shown in Figure 1, the analytic ascendancy model [41], proposed by [PERSON], classifies the types of analysis depending on the degree of analysis challenges and values into four types: descriptive, diagnostic, predictive, and prescriptive analysis. Descriptive analysis describes facts that have occurred in the past or present based on various criteria. Diagnostic analysis determines the cause of a specific problem. Predictive analysis predicts the future or grasps the possibility of unknown results. Prescriptive analysissuggests alternatives for predicted situations. Various studies have been conducted using analytic ascendancy models to classify analysis systems or analysis services in the fields of manufacturing, architecture, and education. [PERSON] et al. [42] introduced the results of a survey on the data analysis status of manufacturers located in Switzerland and Germany. A survey of 100 manufacturing companies was conducted with questions such as the progress status of data collection and processing and the impact of data analysis results. Then, the analysis level of each manufacturer was classified using [PERSON]'s analysis ascendancy model in the process of analyzing the survey results. The survey showed that 41, 40, 15, and 4 companies perform data analysis of the descriptive/diagnostic/predictive/prescriptive analysis level, respectively. [PERSON] et al. [43] proposed a general reference model, consisting of physical, sensing, networking, and computing technology, to address the limitation of the conven \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{2}{c}{**Object**} & \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Attributes of (Object \(\times\) Time \(\times\) Location)**} \\ \cline{2-2} \cline{5-6}  & **Moving** & & & & & O \\ \hline \multirow{2}{*}{This study} & O & O & O & O & (State of moving and unmovable objects, \\  & & & & & such as speed of vehicles and signal of traffic lights) \\ \hline
[33] & O & X & O & O & (Speed of moving objects) \\ \hline
[34] & O & X & O & O & X \\ \hline
[35] & O & X & O & O & (Speed of moving objects) \\ \hline
[36] & O & X & O & O & (Speed and direction of moving objects) \\ \hline
[37] & O & X & O & O & X \\ \hline
[38] & O & X & O & O & X \\ \hline
[39] & O & X & O & O & X \\ \hline
[40] & O & X & O & O & (Duration time of moving objects) \\ \hline \hline \end{tabular} O: addressed; X: not addressed.  
\end{table} 
Table 1: Comparison of the proposed model with conventional models. Figure 1: Analytic Ascendancy Model. tional Internet of Things analysis platform that manages smart space. The proposed model was built using digital twin technology. Then, four twins were constructed by employing the concept of analytics ascendancy models. The four twins provided statistics of sensors, correlation between sensors, prediction of room occupancy or sensors, and solutions for the most efficient smart space management. Further, the four twins were tested by applying it to TellUs at Oulu University in Finland. The results showed that they contributed to the improvement of smart space quality by presenting additional analysis methods than existing IoT platforms. [PERSON] et al. [44] proposed a smart learning analysis system used by the faculty and students in the computer science/information system department curriculum at Bradley University in the U.S. The proposed system has an analytical engine that is based on existing student data for efficient science-technology-engineering-mathematics training. By applying the analytics ascendancy model, the engine is classified into four categories: Descriptive, diagnostic, predictive, and prescriptive analytics. Each analytics provides statistical methods, search and sorting algorithms, statistical models, machine learning algorithms, and simulation and optimization algorithms. A survey of 61 undergraduate and 42 graduate students was conducted on the effectiveness of the proposed system.