# A Big Data and Time Series Analysis Technology-Based Multi-Agent System for Smart Tourism
## Proposed System Architecture and Involved Technologies
### Two-Stage Time Series Prediction Algorithm with OntoDMA  
The OntoDAM [5] obtains the relevant object-action pairs, or uses the Jaccard index, to convert inquiry keywords into corresponding values according to the relevant semantic location and distance  
Figure 9: Proposed mapreduce operation. of ontology. The two-stage procedure of prediction rule generation, as based on the changes to cases generated by OntoCBRA, are as follows. 1. Analysis for specific period and regular location: This study adopts time series analysis to select the cases of specific semantic periods and locations as the alternative information, which is also the greatest difference between this study and traditional information systems: the system operation interface automatically has the function of self-explanation with the assistance of semantic technologies. 2. Specific inquiry type analysis driven by the above events: according to the above definition of the combination of alternative information in Step (1), it is necessary to select alternative case information (\(C_{1}\)\(\sim\)\(C_{n}\)), which has a period longer than that of case generation; then, the \(V_{\mathit{UIP}}\) and \(V_{\mathit{DOWN}}\) of the corresponding values of the same inquiry type are made into corresponding prediction rules \(P(C_{1})=\{C_{1}(V_{\mathit{DOWN}}),\,C_{1}(V_{\mathit{UIP}})\}\), \(P(C_{2})=\{C_{2}(V_{\mathit{DOWN}}),\,C_{2}(V_{\mathit{UIP}})\}\),..., \(P(C_{n})=\{C_{n}(V_{\mathit{DOWN}}),\,C_{n}(V_{\mathit{UIP}})\}\); the corresponding \(V_{\mathit{UIP}}\) and \(V_{\mathit{DOWN}}\) are sequenced, and the set after sequencing is called \(SV_{i}\) (sorted values); finally, the maximum and minimum in \(SV_{i}\) will be the prediction rule \(P(SV_{i})=\{SV_{i}(V_{\mathit{MAX}}),\,SV_{i}(V_{\mathit{MIN}})\}\) for specific events and inquiry type in the period. The rule analysis and generation procedures are shown in Equation (4). All prediction rule sets are integrated into a prediction rule base, and the corresponding Web services are used to obtain prediction solutions. \[ \begin{split} P(C_{1})&=\{C_{1}(V_{\mathit{DOWN}}),\,C_{1}(V_{\mathit{UIP}})\}\\ P(C_{2})&=\{C_{2}(V_{\mathit{DOWN}}),\,C_{2}(V_{ \mathit{UIP}})\}\\ P(C_{3})&=\{C_{3}(V_{\mathit{DOWN}}),\,C_{3}(V_{ \mathit{UIP}})\}\\ &\ldots\\ P(C_{n})&=\{C_{n}(V_{\mathit{DOWN}}),\,C_{n}(V_{ \mathit{UIP}})\}\\ &\textit{Sorting by the value of }V_{\mathit{DOWN}}\textit{ and }V_{\mathit{UIP}},\textit{ and to be the set of }SV_{i}\\ P(SV_{i})&=\{SV_{i}(V_{\mathit{MAX}})\,\,=\,\,Max( \sum\limits_{i=1}^{n}SV_{i}),\,SV_{i}(V_{\mathit{MIN}})=Min(\sum\limits_{i=1} ^{n}SV_{i})\}\end{split} \] (4)  
During the online operation of OntoDMA, the inquired information is transmitted from the Solution Finder in Ubi-IA; then, the information inquiry is conducted in relevant sub-agent systems one by one according to the abovementioned smart inquiry and decision-making processing. First, the time series is adopted to analyze the information value (\(V_{\mathit{pro}}\)); then, the corresponding prediction rules (\(P_{\mathit{Ans1}}(SV_{1})-P_{\mathit{AnsN}}(SV_{N})\)) of specific inquiry types (\(SV_{1}-SV_{N}\)) driven by suitable periods and events are obtained from the prediction rule database; after that, the corresponding \(V_{\mathit{MAX}}\) and \(V_{\mathit{MIN}}\) are sequenced, and the set after sequencing is called \(SSV_{i}\); finally, the maximum and minimum in \(SSV_{i}\) are obtained to meet the prediction solution rules \(P(SSV_{i})\) of this information data. After the generation of prediction solution rules, \(V_{\mathit{pro}}\) is compared with corresponding \(SSV_{i}(V_{\mathit{MAX}})\) and \(SSV_{i}(V_{\mathit{MIN}})\). If \(V_{\mathit{pro}}>SSV_{i}(V_{\mathit{MAX}})\) or \(V_{\mathit{pro}}<SSV_{i}(V_{\mathit{MIN}})\), and the value that declines to \(SSV_{i}(V_{\mathit{MAX}})\) or increases to \(SSV_{i}(V_{\mathit{MIN}})\) will be taken as the prediction value of the information data (\(V_{\mathit{pro}}\)).