## 2 Background and Technology
The actual operation is shown in Figure 5, which shows the OutputFormat at the end of the domain ontology service index. From the establishment of Jaccard dissimilarity and the ontological databases (OD) of the real data, the above-mentioned concept was able to realize that the big data analytics of the domain ontology index can support the subsequent system operation. Figure 6 presents the complete framework of this WIAS system [14]. Users can use various Internet channels to transfer relevant information from Ubi-IA [16] to the back-end information system, while this system can play as a control center that answers the questions of energy-saving information by Solution Finder. Ubi-IA is responsible for providing the processing and conversion of cloud query information and for making intelligent query decisions. The so-called \"intelligent query decisions\" provide the cloud solutions corresponding to cloud query. The set-up covers a three-stage intelligent query decision process as follows: (1) Process the query one-by-one through OntoDMA to determine whether there is a cloud forecasting solution; (2) use OntoCBRA to conduct a batch of sequence query processing to determine whether there is a CBR solution; (3) search for external solutions from the Internet via OntoIAS (Ontological Information Agent Shell) [19], which originates from Ubi-IA of this WIAS system, and process the query one-by-one to determine whether there is a forecasting solution in accordance with the default rules constructed by domain experts. The database of this system came from the ontology database OD processed by the parallel reduction mechanism mentioned above, as well as the user-related database supporting the information sharing recommendation. Figure 4: Proposed parallel reduction mechanism. Figure 5: Actual operation diagram of MapReduce. At the beginning of the operation, this system combined the cloud information ontology constructed by domain experts with the corresponding default rules, retrieved the useful information of the words that came out when querying information, and initialized the query information to support the confidence of default rules. At the same time, a similarity algorithm was conducted for the cases corresponding to the WIAS web services support in the cloud information ontology to complete the initialization of the system operation. Once the system started to operate, the system would periodically make a response according to the frequency of querying information [6]. This system employed time-series analysis to calculate the most frequently occurring and the least frequently occurring query information. OntoCBRA generated relevant information of the cases according to the calculation result and matched it with the two-stage time-series forecasting algorithm to trigger the default rules corresponding to OntoDMA adaptation. The domain ontology helped to compare and retrieve the appropriate correspondence in order to effectively add value to the quality of cloud information consultation and sharing, thus enhancing the correctness, authenticity, and integrity of said information. If both OntoCBRA and OntoDMA are unable to appropriately answer the cloud query, then this system will trigger OntoIAS to directly conduct the Preprocess, Map, Shuffle, and Reduce operations of the aforementioned parallel reduction mechanism to complete data research, data retrieval, data classification, and data presentation (or sorting). This system sought out solutions of cloud information from the Internet and added the default rules by domain experts, so as to fully construct the learning cycle in response to query information. Through the three-stage intelligent decisions (OntoDMA, OntoCBRA, and OntoIAS), the purpose of this study--to find a solution for optimal energy-saving information solution--was achieved. This study focused on the development of OntoCBRA under the support of the aforementioned technologies.