A Big Data and Time Series Analysis Technology-Based Multi-Agent System for Smart Tourism

Abstract

This study focuses on presenting a development trend from the perspective of data-oriented evidence, especially open data and technologies, as those numbers can verify and prove current technology trends and user information requirements. According to the practical progress of Dr. What-Info I and II, this paper continues to develop Dr. What-Info III. Moreover, big data technology, the MapReduce paralleled decrement mechanism of the cloud information agent CEOntoIAS, which is supported by a Hadoop-like framework, Software R, and time series analysis are adopted to enhance the precision, reliability, and integrity of cloud information. Furthermore, the proposed system app receives a collective satisfaction score of 80% in terms of Quesenbery's 5Es and Nielsen ratings. In addition, the verification results of the interface design show that the human-machine interface of our proposed system can meet important design preferences and provide approximately optimal balance. The top-n experiment shows that the top-5 recommendations would be better for solving the traditional tradeoff between output quality and processing time. Finally, the system effectiveness experiments indicate that the proposed system receives an overall up-to-standard function rate of 87.5%, and such recommendations provide this system with high information correctness and user satisfaction. Although there is plenty of room for improvement in experience, the feasibility of this service architecture has been proven.

A Web Services, Ontology and Big Data Analysis Technology-Based Cloud Case-Based Reasoning Agent for Energy Conservation of Sustainability Science

“…The above-mentioned was the basic structure of the knowledge of this system. Furthermore, this study used Jaccard similarity [17] to estimate the consistency between ontological concepts, using the consistency between the concept of the retrieved words and the corresponding concept of WordNet and its related position to index the domain concept. Finally, it adopted the identification code Synset_ID in WordNet to access the domain concept and support the overall system operation.…”

“…This study also proposed a parallel reduction mechanism [17] based on big data analytics and divided it into four steps: (1) Generate Preprocess works for the keyword sets corresponding to individual websites; (2) Apply domain ontology and Jaccard dissimilarity to get Maps representing three keyword sets on individual websites; (3) Sort Shuffle corresponding to the optimal three keyword sets on individual websites; and (4) Use the average output of Jaccard dissimilarity and the closest three corresponding keywords that users have inquired to conduct the works of Reduce, as shown in Figure 4. This study also proposed a parallel reduction mechanism [17] based on big data analytics and divided it into four steps: (1) Generate Preprocess works for the keyword sets corresponding to individual websites; (2) Apply domain ontology and Jaccard dissimilarity to get Maps representing three keyword sets on individual websites; (3) Sort Shuffle corresponding to the optimal three keyword sets on individual websites; and (4) Use the average output of Jaccard dissimilarity and the closest three corresponding keywords that users have inquired to conduct the works of Reduce, as shown in Following the above literature, this study took the semi-open source framework Hadoop (such as Dropbox) as the context and explored the concept of "R + Hadoop = Big Data Analytics" [18]. This study also built the above-mentioned MapReduce parallel reduction mechanism, integrated the computing between the keywords of domain ontology services support and the corresponding Jaccard dissimilarity, and then supported the various information services of this system based on the WIAS big data analytics technique.…”

“…(a) (b) In short, energy conservation is one of the important topics for sustainability science, while casebased reasoning is one of the most important techniques for sustainable processing. This study focused on developing a cloud case-based reasoning agent built on web services, ontology, and big data analytics [15,17]. This study first introduced the technology related to the construction of web services platforms and then explored how to use this technology to widely and seamlessly integrate and support the cloud interaction between the back-end information multi-agent systems.…”

A Cloud Information Monitoring and Recommendation Multi-Agent System with Friendly Interfaces for Tourism

“…This study adopted WordNet (http://wordnet.princeton.edu/) as the base of the comparison model in combination with the Chinese-English bilingual ontology word network (https://ckip.iis.sinica.edu.tw/CKIP/engversion/index.htm) of Taiwan's Academia Sinica to discuss the transformation of Chinese-English bilingual information, the link between language information and the conceptual framework, and the link between word sense differentiation and word sense relationship, as well as their fields of use, in order to provide the basic framework for knowledge management in this system. Jaccard similarity (or Jaccard index) was introduced to estimate the consistency between ontological concepts [5,6]. The basic concept is to index the domain concept by using the consistency between the concept of the search term and the corresponding concept of WordNet and its related position.…”

“…In the earlier stage of this study, the solution through the Dropbox cloud space, which was researched and developed as an open data platform, and did not provide APIs for accessing the corresponding database, but only provided hyperlinks for downloading the open database. The solution to this issue is UAI technology, and the related steps are described in [5,7]. The establishment of UAI access to the open database by the subsequent system only requires the conversion of the location of the user's local GPS to the corresponding address; it then compares the appropriate open database for the keywords of the user's current event, and then converts the corresponding map display according to the data intercepted in the "address" field to reach the research goal of the aforementioned value-added location-based mobile information service.…”