## 4 System Presentation and Effectiveness Analysis  
### System Presentation  
The development environment and tools of the proposed system include Java SE Development Kit, AppServ, JCreator LE, Microsoft Visual Studio, Microsoft SQL Server, etc. The back-end servo system is IBM System x3500 M2. At the beginning of system operation (periodic off-line), the researcher selected the case monitor page, entered the time (months, days, or hours) of the conversion in the case base, and carried out the detection. When the detection was completed, the total number of times of the processing by the case monitor was displayed by the \"Count\" in the screen, and each step in the process of off-line operation was presented, including making an \"inquiry\" on environmental sensing data that has not been converted into a case (Figure 12). Next, the data were \"converted\" via defining the contents with semantic meaning (Figure 13). In the end, the Sensor_Data and its corresponding operation mode were retrieved, and the cases were integrated and stored into a \"case base\" in accordance with the types of the sensor, such as Humidity, Temperature, Illumination, and CO2 (Figure 14). Figure 11: Operational flow of the case retainer. Figure 12: Off-line: Inquiry. As described in the aforementioned three-stage intelligent decisions, OntoDMA processed the query information that did not appear frequently or did not exist. At that time, OntoCBRA only launched the corresponding web services CBR _InsTmpCaseData provided by WIAS and recorded the information value of this sequence to Case_TMP to complete the processing. If OntoDMA was unable to carry out the processing, then OntoCBRA would be triggered by the solution finder of Ubi-IA. Through the web services CBR_Solutions offered by WIAS, OntoCBRA then launches the online case application. This system sorted all of the \"MAC_Type_Name\" in this space in accordance with Area_Name, Area_Type_Name, and Area_Size_Name and calculated the average values of \"avgVALUE\", \"avgvRangeUp\", and \"avgvRangeDown\". In order to expand the range of case retrieval, this system used \"avgVALUE\" as the base. Multiplying the value of \"avgVALUE\" by 75% became \"avgvRangeDown\", while multiplying by 125% became \"avgvRangeUp\". The design of avgvRangeDown and avgvRangeUp can avoid the situation in which no case is available when using avgVALUE as the base. In other words, the range of case retrieval equals the range between avgvRangeDown and avgvRangeUp (Figure 15). Figure 14: Off-line: Case Base. Figure 13: Off-line: Convert. Figure 15: Case retrieval range. In the sector of case retrieval, OntoCBRA took avgvRangeUp and avgvRangeDown as the threshold of similarity (75% to 125%). Figure 16 shows all cases that met the threshold. If no case met the similarity threshold, then it means that this system had no experience on processing a similar situation, and CBR Agent sent a null response to Ubi-IA. With the support of the system ontology database, this system disassembled the cases meeting the threshold and computed the similarity between the data of these cases and the sequence monitoring data in Case_TMP, as shown in Figure 17. If the data of these two were \"identical\"--that is, the value of similarity was 1 (100%)--then this system would directly return the answer of this case to Ubi-IA as the solution of the sequence information value. The step of case reuse was thus completed if the two were \"partially similar but not identical\", then this system entered into the sector of case trimming. If there was no identical case in the step of OntoCBRA, then the system would trim the complete similarity of cases, as shown in Figure 18. The column \"SimilaryCount\" indicates the sum of the \"Similarity\" of the same \"Case_ID\", while the column \"SimilaryAvg\" represents the average value of the similarity.