## 4 System Presentation and Effectiveness Analysis
### System Effectiveness Analysis  
The experimental content was utilized to test the performance of OntoCBRA and its learning effectiveness. However, this study used the similarity value threshold as the trigger for case reuse, case trimming, and experts seeking. It was thus necessary to conduct the analysis with the usage rate of different similarity values at each level, carry out the on-board testing over a long term, and adjust the threshold of similarity value. At the end of the analysis, the similarity value threshold was summarized, as shown in Table 6. This threshold was applied to the complete energy-saving information agent system with the front-end and the back-end to analyze the performance of the system [20]. First, environmental sensing data from the energy-saving demonstration classroom in the Electrical Engineering and Computer Science Building of St. John's University were collected in 2011. In total, 68,000 or more monitoring records were collected and converted into the internal record format and stored in the system case database, shown as Figure 20. The first experiment focused on the performance effectiveness of OntoCBRA and used the aforementioned records as its training dataset. Based on the \"CBR-TransRawtoCase\" service provided by the WIAS web service, the system established a total of about 4000 cases in the case base. From these test data, 6500 queries were randomly selected as samples to test the performance of OntoCBRA. The results of the five experiments were examined by domain experts, who are energy saving experts, to determine whether the reasoning results of the proposed system meet the definition and requirements of energy saving. As shown in Table 7, this system had four levels: (1) Direct use of the recommended cases (with 100% similarity); (2) case \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Level 1 Identical** & **Level 2 Case Reuse** & **Level 3 Case** & **Level 4 Experts** \\  & & **Trimming** & **Seeking** \\ \hline \hline \multirow{2}{*}{Scope of testing} & \multirow{2}{*}{100\%} & \multirow{2}{*}{100\% \(>\) SR(V) \(\geq\) 85\%} & \multirow{2}{*}{85\% \(>\) SR(V) \(\geq\) 75\%} & \multirow{2}{*}{SR(V) \(>\) 75\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Similarity value threshold. Figure 19: Output in the trimming step. Figure 18: Trimming of complete similarity of cases. reuse (with similarity over 85% but less than 100%); (3) case trimming (with similarity over 75% but less than 85%); (4) experts seeking (with similarity less than 75%). Table 7 shows that for an average of 2520.4 queries, Level 1 can process 50.2 queries on average, while the average queries processed by Level 2, Level 3, and Level 4 are 1310.4, 914.6, and 243.4. The result proved that OntoCBRA processes an average of about 40% of the inquiries, leaving only about 60% of the inquiries to the back-end system for processing, thus effectively reducing the common burdens of back-end server systems. The second experiment focused on the learning effectiveness of OntoCBRA cases. This study used the same system as the first experiment and the cases in the case reuse experiment 1 and subsequently collected more than 34,000 monitoring records from the 2016 records. This study then randomly selected 6500 records from these materials to conduct five environmental query experiments. Table 8 shows the results of the five experiments that were reviewed by domain experts one by one. In Table 8, for an average of 2534.4 inquiries, 53.8 inquiries were completed at Level 1, 1342.6 inquiries at Level 2, 901.8 inquiries at Level 3, and 236.2 inquiries at Level 4. An average of 41% of inquiries was completed in OntoCBRA.