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

applied sciences

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

Kune-Yao Chen 1 and Sheng-Yuan Yang 2,*

1 Department of Information Management, St. John’s University, New Taipei City 25135, Taiwan; [email protected] 2 Department of Information and Communication Engineering, St. John’s University, New Taipei City 25135, Taiwan * Correspondence: [email protected]; Tel.: +886-2-2801-3131 (ext. 6396)

Received: 8 July 2019; Accepted: 15 October 2019; Published: 17 October 2019

Abstract: The tourism statistics of Taiwan’s government state that the tourism industry is one of the fastest growing economic sources in the world. Therefore, the demand for a tourism information system with a friendly interface is growing. This research implemented the construction of a cloud information service platform based on numerous practical developments in the Dr. What-Info system (i.e., a master multi-agent system on what the information is), which developed universal application interface (UAI) technology based on the Taiwan government’s open data with the aim of connecting diﬀerent application programming interfaces (APIs) according to diﬀerent data formats and intelligence through local GPS location retrieval, in support of three-stage intelligent decision-making and a three-tier address-based UAI technology comparison. This paper further developed a novel citizen-centric multi-agent information monitoring and recommendation system for the tourism sector. The proposed system was experimentally demonstrated as a successful integration of technology, and stands as an innovative piece of work in the literature. Although there is room for improvement in experience and maybe more travel-related agents, the feasibility of the proposed service architecture has been proven.

Keywords: wearable devices; GPS; linked open data; cloud tourism multi-agent systems

1. Introduction Yalçınkaya et al. [1] and the Encyclopedia of the MBA Think Tank (https://wiki.mbalib.com/w/ index.php?title=Smart_Tourism&redirect=no) noted that smart tourism is based on a new generation of information technology (also called information and communication technologies, ICT) to meet the personalized needs of tourists and provide high-quality and high-satisfaction services; it has also been considered that smart tourism is a part of a smart earth and smart city, such as that which is described in [1]. Meeting the personalized needs of mass tourists, achieving seamless integration of tourism public services and management, and providing services for enterprises (especially small- and medium-sized enterprises) are the three major goals of intelligent tourism. Most of its core technologies are considered to be the epitome of cloud computing, meaning the Internet of Things (IoT), mobile terminal communication, and artiﬁcial intelligence [2], and it is the integration and application innovation of the new generation of information technology. Alongside the tourism statistics of Taiwan’s government, the 2017 Tourism Competitiveness Report, as released by the World Economic Forum (https://www.weforum.org/), states that the tourism industry is one of the fastest growing economic sources in the world, accounting for 10% of the global GDP and creating over 300 million jobs.

Appl. Sci. 2019, 9, 4385; doi:10.3390/app9204385 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 4385 2 of 24

As Taiwan is facing fast-growing business opportunities created by 10 million potential international tourists, it must consider the future trend of the IoT and develop a complete solution for smart tourism with high added value and a global exemplary effect in combination with Taiwan’s advantages in the ICT industry. The handling of unexpected or urgent events involving tourism was the biggest motivation for this study to explore the field of smart tourism in the hopes of autonomously providing corresponding monitoring and recommendation information. With 2014 as the beginning of the era of the data economy, Gartner (a world-renowned IT industry consultant company that provides an important industry perspective) announced the top 10 technology trends (http://www.gartner.com/technology/research/predicts/), where a clear axis has gradually emerged, including community, action, big data, and the cloud; these four driving forces will be intertwined and change future technological trends. However, according to Ylijoki and Porras (a view of academic trend) [3] and IBM (a view of industry trend), big data has four characteristics (the 4V of big data), the “volume”, “velocity”, “variety”, and “veracity” of data (https://www.ibm.com/analytics/hadoop/big-data-analytics?). Thus, in today’s information network era, the characteristics of such data have become more in number, fast, miscellaneous, and difficult to distinguish between real and fake. Therefore, if we can borrow “cloud” technical support; introduce the latest, correct, and complete “open government data”; and provide corresponding information value-added services through “mobile” GPS (global positioning system) equipment positioning technology, the effect of monitoring and recommending the best information as a “community” can be realized. The most important driving force for this research is to cite the four driving forces above and explore a cloud information monitoring and recommendation multi-agent system with both technological trends of industry and academia to meet the real needs of information users. Open data refers to carefully selected and publicly licensed data, which is not subject to copyright, patent rights, or other regulatory mechanisms, and can be freely published and used by the public without restriction. However, the term “open data” did not appear until recently, due to the rise of the Internet. After the U.S. government established open data government organizations, such as data.gov, relevant open databases have flourished, such as data.gov.uk—the U.K. government’s open data website; data.gov.tw—Taiwan government’s open data platform, among other databases. Linked open data (LOD) is the vocabulary and technology derived from the linking open data W3C SWEO (World Wide Web Consortium Semantic Web Education and Outreach) Community Project, as initiated by Auer et al. [4], which confirmed that the data are organized according to the linked data principle, and released the information in an open and authorized manner. However, to date, the data formats of Taiwan’s government data open platform are mostly XML (Extensible Markup Language), JSON (JavaScript Object Notation), and CSV (Comma-Separated Values, or xls), and the access methods are file download offline or online direct access via the system API (application programming interface), while the latter has a limited number of accesses (about 2000 pieces of data). Therefore, depending on the different data formats and intelligent interfaces for different system APIs, this research proposed a UAI (universal application interface) technology [5] based on the Taiwan government’s open data to solve the problem of using pre-storage space and reduce the system processing time. Through local GPS location retrieval, and with the support of OpenData@Taiwan/Taipei and domain ontology, the appropriate open information is compared and intercepted, and then, the corresponding location-based services (LBS) are added, such as providing open information for medical treatment, warnings in case of accidents, or emergencies during travel. This study is based on mobile phone GPS positioning, and the reason why the quality of “integrating linked open data to enhance LBS” should be improved is self-evident (i.e., the recommendation information is credible and correct). The rise of the IoT in recent years has been accompanied by the popularity of wearable devices. According to Gartner (https://www.gartner.com/en/newsroom/press-releases/2018-11-29-gartner-says- worldwide-wearable-device-sales-to-grow-), a market research organization, 225 million wearable devices will be sold globally in 2019, with an annual growth rate of 25.8% versus 179 million in 2018. In terms of sales amount, wearable electronic devices can generate revenues of USD$42 billion, of Appl. Sci. 2019, 9, 4385 3 of 24 which USD$16.2 billion is contributed by smart watches. In an information society, where wearable devices are popular, more and more consumers are willing to accept personal health information management with the awakening of their health awareness; therefore, there is a growing demand for the system to provide corresponding monitoring and management information systems. In a report in the Common Wealth Magazine on CES (Consumer Electronics Show) 2016 (https://www.ces.tech/News/ Press-Releases/CESPress-Release.aspx?NodeID=2d11a459-e026-409e-a828-3d0e9212cb68), intelligent health networking devices will become the mainstream of products in the future. As the new generation of wearable products become more popular, content will focus more on information services for personal health management; for example, when traveling, the system can provide the location of a nearby sports center. Therefore, this research provides an appropriate personal health information monitoring and consulting agency function in the system through a “wearable device” to further explore how to achieve the related technology of comprehensive intelligent tourism cloud information consultation and a sharing multi-agent system, on the basis of wearable devices, GPS, and linked open data. On the basis of the aforementioned information and the plans of Taiwan’s Ministry of Science and Technology Dr. What-Info (i.e., a master multi-agent system on what the information is): research and development of an intelligent mobile information consultation and sharing multi-agent system (MOST-103-2221-E-129009), and Dr. What-Info II: research and development of an intelligent mobile information consultation and sharing multi-agent system based on Taiwan government open data with universal application interface technology (MOST-104-2221-E-129-009), the relevant completion objectives are summarized and described in [6–8]. This paper further develops a travel cloud information monitoring and recommendation multi-agent system on the basis of wearable devices that include GPS and LOD to explore the LOD access technology suitable for OpenData@Taiwan, thereby integrating this UAI technology chain for wearable devices to increase the accuracy, authenticity, and integrity of intelligent tourism information with the support of three-stage intelligent decision-making, and effectively adding value to the quality of cloud information monitoring and recommendation. In short, as Eric E. Schmidt, Google’s chief executive officer, said at The Mobile First World Asia Pacific event in November 2014, “Action revolution has driven the industry from web first in the past to mobile first. The future is a world of mobile only.” This same concept is also mentioned in the article by Shah et al. [9]. This was the initial concept, and it is expected to develop from the relevant practical technologies of mobile devices with the support of the powerful information agent system CEOntoIAS (Cloud Extension of OntoIAS—Ontological Information Agent Shell) [10,11] at the back end for solving the problem that mobile devices cannot provide effective information services due to the lack of computing power. The proposed system has friendly interfaces and can refer to the aforementioned UAI-based LOD access technology in collaboration with GPS location interception, and with the support of appropriate linked open information and domain ontology indexing for autonomously providing corresponding information, as well as effectively improving the monitoring and recommendation qualities of the system’s mobile information.

2. Literature Analysis and Discussion and Development Technology Wearable devices, introduced in 2013, contain miniature electronic devices that can sense, wear, display, calculate, and carry out corresponding information and activities through a network. The basic core function includes an attachment device, physiological sensing, activity sensing, and environmental sensing, among others. At present, the relevant application markets include various professional and special ﬁelds, such as information, entertainment, health and ﬁtness, medical treatment and care, and safety and security. There is plentiful relevant literature in Taiwan and abroad on this topic, such as Ke’s [12] analysis of the management of health willingness through wearable devices and exploration of the development trend of its application in personal health insurance. On the basis of a smart watch life log application for diabetics, Preuveneers and Joosen [13] explored technologies that use cloud and homomorphic encryption to ensure privacy. Adapa et al. [14] examined the factors associated with the adoption of smart wearable devices, especially Smart Glass (Google Glass) and Smart Watch Appl. Sci. 2019, 9, 4385 4 of 24

(Sony Smart Watch 3). Jiang et al. [15] put forward a user-centric three-factor authentication scheme to ensure the privacy and security of sensitive data for wearable devices assisted by a cloud server. As mentioned above, wearable devices are becoming an indispensable part of future human-oriented information services, especially in the personal health sector. For this reason, this study also refers to the personal recording function (e.g., calorie consumption monitoring) of a wearable device, and provides appropriate information services (e.g., corresponding sports centers or places) for the wearer in a travel itinerary, which completely portrays all-around intelligent travel information service. Open data itself is not a new concept. Generally speaking, the application of open data is mainly non-textual data material, which is often opened because the data have commercial value, or it can become a valuable product after consolidation. LOD is an applied open database organized by the linked data principle. There are also many open data or LOD-related studies in the literature; for example, Yu [16] referred to the traffic class parking lot of the Taipei City open data platform YouBike (YouBike: Smile Bike is a public bicycle rental system in Taiwan), and MRT (Mass Rapid Transit) to explore the related technologies of linked open data application. Alobaidi et al. [17] proposed using LOD to enrich query content and improve the effectiveness and ranking of searches. Musto et al. [18] proposed a methodology to automatically feed a graph-based recommender system with features gathered from the LOD cloud, and analyzed the impact of several widespread feature selection techniques in such recommended settings. Sansonetti et al. [19] described a hybrid recommender system in the artistic and cultural heritage area, which considers the activities on social media performed by the target user and their friends, and takes advantage of linked open data sources. Most of the above studies mentioned value-added follow-up information services with LOD support; under the support of the WIAS (web service-based information agent system) cloud platform [11], this study also referred to UAI technology in order to retrieve and translate according to the local GPS location and the three-tier address-based comparison technology, and intercept appropriate corresponding LOD information to support the operation of overall information processing, exchange, communication, operation, integration, analysis, and decision-making support, thus, enhancing the correctness, authenticity, and integrity of LBS information. With the advancements of the technology era, handheld devices can receive calls, send and receive messages, and provide music and ringtone downloads, multimedia messages, video calls, and other functions. In addition, information and related services at specific locations are provided through wireless networks, which is called LBS. However, LBS was originally a rescue service provided by U.S. operators in 1996, and at that time was called E911 [20]. The applications of LBS explored in the literature include Hsu [21], who used the service quality model (SERVQUAL) to explore the relationship between Uber’s LBS quality and satisfaction, as well as its related applications. Wang et al. [22] introduced the issues of location awareness and privacy protection, and explored technologies related to LBS when users have different requirements based on different locations. Sun et al. [23] introduced LSA (long-term statistical attack) by using historical data, and offered two methods to preserve users’ privacy, including dividing the regions of the map into different PLs (levels of protection requirements) according to the privacy requirements, and analyzing the ability to preserve user’s privacy by entropy. Jang and Lee [24] presented the features of LBS that influence the usage intentions of users and the moderating effects of innovativeness for LBS in sustainable mobile-related industries. On the basis of the above LBS documents, it is known that the location-based service, mobile positioning service, and location service can obtain the location information (geographic coordinates) of the mobile terminal user through the mobile operator’s radio communication network (e.g., GSM- Global System for Mobile Communications network or CDMA- Code Division Multiple Access network), or an external positioning method (e.g., GPS), and provide users with corresponding value-added information services with the support of the back-end GIS (geographic information system) platform. In this study, under the support of the CEOntoIAS cloud platform, the UAI technology was used to retrieve and translate according to the local GPS location and the three-tier address-based comparison technology to intercept appropriate corresponding LOD information; support the operation of overall information Appl. Sci. 2019, 9, 4385 5 of 24 processing, exchange, communication, operation, integration, analysis, and decision-making support; and enhance the correctness, authenticity, and integrity of LBS information, thus effectively adding value to the quality of the cloud information monitoring and recommendation in the system. Most of the aforementioned systems proposed an appropriate agent system architecture for users of specific jobs and cannot be adapted to the changing operating environment. Moreover, most of them also used a specific language to develop a specific agent system, which has poor functionality expansion and is difficult to maintain. Web service is the technology proposed to solve the above problems, as it is the foundation for a cloud (mobile) information system to be pushed to the extreme. There are many systems that discuss the cloud (mobile) information architecture in the literature; for example, Sun [25] proposed an internet service composition system based on adaptive trust and a reputation model of dynamic social networks to explore how to ensure and monitor the quality and reliability of service composition under the system heterogeneity and dynamic characteristics of the services. Wei et al. [26] explored relevant technologies for building interoperable and extensible text-mining network services on the basis of the concept of exceeding accuracy. Yoshiura et al. [27] developed and implemented an acceptable, useful, and transparent web-based information system for a regional mental healthcare service network in a medium-income country with a decentralized public health system. Ghavami [28] provided an advanced traveler information system on the basis of open geospatial consortium standards to perform fuzzy bi-criteria shortest-path analysis for a multimodal transportation network. This study was also based on the support of web service technology to construct the cloud web service information agent system WIAS, which is designed and constructed to provide the corresponding web services for the operation of related subsystems. Many flexible system designs are also cited, such as standardized and flexible parameter transfer formats, quickly disassembled and reorganized SQL IC (structured query language integrated circuits), and modular network service function design. All these designs positively present the excellent performance of web service technology. A cloud (mobile) intelligent information processing and decision support multi-agent system was also proposed to discuss the feasibility of supporting specific cloud information services, as well as related implementation technologies and issues, in order to achieve the research scope of integrating web service technology and a multi-agent system architecture. In this era of information explosion and confusion, the demand for information monitoring and recommendation technology is growing, such as that suggested by the systems proposed in [29,30]. Therefore, on the basis of the many practical advances in the research, development, and implementation of the Dr. What-Info system in the early stage, this paper enhanced a multi-agent system with friendly interfaces for intelligent travel cloud information monitoring and recommendation based on wearable devices, GPS, and linked open data. Regarding the aforementioned travel needs, this paper prioritizes the completion of the three most basic and important tourism-related subagents, including a personal health information monitoring and consulting agent (PHI agent), a restaurant information consultation and sharing agent (RI agent), and a beverage safety consultation agent (BS agent), in order to meet the basic travel needs of users for this food kingdom in Taiwan (for example, the recommendation of gourmet restaurants or the monitoring and recommendation of overeating in the face of food). These subagents explore LOD access technologies for integrating the aforementioned UAI technology chain with OpenData@Taiwan for mobile devices to expand the accuracy, authenticity, and integrity of intelligent tourism information with the support of a three-stage intelligent decision-making strategy. This paper not only extends the application scope of Dr. What-Info, it also attains the ultimate goal of this research—the establishment of an intelligent mobile information monitoring and recommendation multi-agent system with the characteristics of being precise, rapid, tough, universal, and having initiative. Appl.Appl. Sci. Sci.2019 2019,,9 9,, 4385 x FOR PEER REVIEW 6 6of of 23 24

3. Proposed System Technology, Architecture, and Operation 3. Proposed System Technology, Architecture, and Operation 3.1. Domain Ontology and Related Ontology Services 3.1. Domain Ontology and Related Ontology Services This study used Protégé (http://protege.stanford.edu/) to construct the basic framework of domainThis knowledge, study used Protandé gusedé (http: this//protege.stanford.edu tool to develop many/) to ontologies, construct the including basic framework PC-DIY (Personal of domain knowledge,Computer Do and It used Yourself) this tool ontology, to develop researcher many ontologies, ontology, includingthesis solicitation PC-DIY (Personalontology, Computernetwork Doprotocol It Yourself) packet ontology, format ontology, researcher energy-saving ontology, thesis information solicitation ontology, ontology, and network YOHO (Young protocol Home) packet formatinformation ontology, ontology, energy-saving which informationare used to ontology, verify the and validity YOHO (Youngand correctness Home) information of the ontology ontology, whichconstruction are used to verifymethods the validity and correctnessand of the ontologytools. constructionProtégé methods and tools.API Prot(https://protegewiki.stanford.edu/wiki/WebProtegeUégé API (https://protegewiki.stanford.edu/wiki/WebProtegeUsersGuidesersGuide) was used to )construct was used and to constructenhance andthe enhanceprocessing the technologies processing technologiesfor the relevant for inform the relevantation systems information supported systems by supportedontology. In by addition, ontology. Inthis addition, study constructed this study constructeda reasoning aprimitive reasoning functi primitiveon that function supports that the supportsapplication the of application knowledge of knowledgeontology. Finally, ontology. the Finally,ontology the architecture ontology architecture was utilized was to index utilized actual to index information actual information for rapid and for rapidaccurate and access accurate to accessrelevant to information relevant information (see Figu (seere 1 Figurefor the1 indexfor the concept). index concept). First, Figure First, 1a Figure shows1a showshow the how ontology the ontology index is index used isto used index to relevant index relevant files, then files, to filter then the to filter documents the documents that do not that contain do not containthis ontological this ontological word with word the with partial the full-text partial full-text index, and, index, finally, and, finally,to narrow to narrowthe search the scope search of scope the ofdocuments the documents in two in stages. two stages. This approach This approach can accurately can accurately judge useful judge information, useful information, as ontology as ontology has the hasprecise the precisemeaning meaning of domain of words, domain and words, can quickly and can index quickly and indexfilter useful and filter information useful information by combining by combiningit with real it data with to real reach data the to goal reach of the ontology, goal of meaning ontology, it meaning supports it fast supports and accurate fast and access accurate to useful access toinformation useful information (see Figure (see 1b Figure for de1tails).b for details).Furthermore, Furthermore, the system the further system modularizes further modularizes the relevant the relevantusage information usage information according according to different to di ffusageerent usagesituations, situations, which whichfacilitates facilitates the quick the quickaccess access of the of thesystem, system, and and thus thus effectively effectively supports supports the theoveral overalll system system operation, operation, as shown as shown in Figure in Figure 1c. 1c.

Ontology Index Document numbers of Partial Full-Text Index (Inverted Index of Ontology documents in system (Inverted Index of Partial Feature Terms) Terms) DocNo 1 Term 1 Term 11 DocNo 2 Term 2 Term 21 DocNo 3 Term 3 Term 31 DocNo 4 ...... (a)

Tourism-Journey

consist of consist of

Country-Area ... consist of consist of each consist of concept Concept_Name

T_No_Of_A CA1 CA2... CAn Transpotation Accommodation Destination n ...... isa isa isa isa Name Type ... Address Attribute Value Aircraft Wheel Hotel Home-Stay Telephone Value1 Satisfaction1 Source1 . Residence-Time Attribute Value ... Transport-Way . Company isa isa Category Room-Type Value1 Satisfaction1 Source1 Departure-Time Room-Type Room-Name Attribute Value ... Arrival-Time Room-Name Telephone Value1 Satisfaction1 Source1 . Train Vehicle Class Telephone FAX Value2 Satisfaction2 Source2 . FAX Weekday-Price Value3 Satisfaction3 Source3 . Line First-run-Hour Weekday-Price Holiday-Price Value4 Satisfaction4 Source4 . Holiday-Price Class Last-run-Hour Value5 Satisfaction5 Source5 . Direction Time-Interval ... Departure-Time Arrival-Time (b)

Figure 1. Cont. Appl. Sci. 2019, 9, 4385 7 of 24 Appl. Sci. 2019, 9, x FOR PEER REVIEW 7 of 23

Disease General Emergency Loss Accident Event Event Event Event

Real Usage Index Tourism Journal Internal Reasoning Index

Food, Medical Aid, Medical Aid, Clothing, Outposted Police Help, Police Help, Housing, Agency Event Event Transportation, Help, Call Inform, Inform, Education, Police Help Event Report Event Report Entertainment (c)

FigureFigure 1.1. Tourism ontology ontology index index structure structure and and data data relationship: relationship: (a) (aa two-stage) a two-stage index index structure; structure; (b) (brelationship) relationship between between ontology ontology inde indexx structure structure and and real real data; data; and and (c ()c modular) modular connection connection and and use use of of contextcontext index index inin tourismtourism eventevent ontology.ontology. Ontological services include the semantic distance conversion of search words and conversion Ontological services include the semantic distance conversion of search words and conversion services,services, such such as as search search words words corresponding corresponding to upper to upper words, words, lower words,lower synonyms,words, synonyms, and antonyms. and Thisantonyms. study adoptedThis study WordNet adopted (http: WordNet//wordnet.princeton.edu (http://wordnet.princeton.edu/)/) as the base of theas comparisonthe base of model the incomparison combination model with in the combination Chinese–English with the bilingual Chinese–English ontology word bilingual network ontology (https: word//ckip.iis.sinica. network edu.tw(https://ckip.iis.sinica.edu.tw/CKIP/engversion/index.htm)/CKIP/engversion/index.htm) of Taiwan’s Academia of SinicaTaiwan’s to discuss Academia the transformationSinica to discuss of Chinese–Englishthe transformation bilingual of Chinese–English information, bilingual the link information, between language the link information between language and the information conceptual framework,and the conceptual and the linkframework, between and word the sense link dibetwfferentiationeen word andsense word differentiation sense relationship, and word as wellsense as theirrelationship, fields of use,as well in order as their to providefields of the use, basic in order framework to provide for knowledge the basic framework management for in knowledge this system. Jaccardmanagement similarity in (orthis Jaccard system. index) Jaccard was similarity introduced (or Jaccard to estimate index) the was consistency introduced between to estimate ontological the conceptsconsistency [5,6 ].between The basic ontological concept isconcepts to index [5,6]. the domainThe basic concept concept by is using to index the consistencythe domain betweenconcept by the conceptusing the of theconsistency search term between and the the corresponding concept of the concept search of term WordNet and the and corresponding its related position. concept Finally, of theWordNet domain and concept its related is supported position. by the Finally, identifier the Synset_ID domain concept in WordNet is supported to support by the the overall identifier system operation.Synset_ID Thein WordNet most important to support factor the is thatoverall users system can employ operation. SQL The and most JWNL important (Java WordNet factor is Library that (http:users// jwordnet.sourceforge.netcan employ /handbook.htmlSQL and)), to accessJWNL the WordNet(Java database,WordNet which isLibrary also the main(http://jwordnet.sourceforge.net/handbook.html)), reason why this study used the SQL database to to access construct the WordNet ontology database, and the Java which development is also the system.main reason Furthermore, why this thestudy position used the of thisSQL domain database concept to construct in WordNet ontology ontology and the is Java indexed development according tosystem.hasURI Furthermore,of the two attributesthe position of ofhasURI this domainand hasConsistency concept in WordNetfor this ontology most representative is indexed according domain concept.to hasURI The of the consistency two attributes of the of domain hasURI concept and hasConsistency is calculated for by this Jaccard most similarity, representative and storeddomain in theconcept. corresponding The consistencyhasConsistency of the domainattribute concept to complete is calculated the corresponding by Jaccard similarity, processing andof stored the domain in the concept.corresponding Finally, hasConsistency according to theattributeSynset_ID to completeof the concept the corresp in WordNet,onding processing the position of of the the domain domain conceptconcept. is Finally, accessed according in the ontology to the Synset_ID database (OD)of the to concept support in the WordNet, overall systemthe position operation of the in domain order to solveconcept the is precision, accessed toughnessin the ontology and universality database (OD) problem to support of the the same overall meaning system indi operationfferent domains. in order to solve the precision, toughness and universality problem of the same meaning in different domains. 3.2. Building UAI-Based LOD Access Technology 3.2. Building UAI-Based LOD Access Technology In the earlier stage of this study, the solution through the Dropbox cloud space, which was researchedIn the andearlier developed stage of asthis an study, open the data soluti platform,on through and did the notDropbox provide cloud APIs space, for accessing which was the correspondingresearched and database, developed but onlyas an provided open data hyperlinks platform, for and downloading did not provide the open APIs database. for accessing The solution the tocorresponding this issue is UAI database, technology, but only and theprovided related hyperlinks steps are described for downloading in [5,7]. Thethe establishmentopen database. of The UAI accesssolution to theto this open issue database is UAI by technology, the subsequent and the system related only steps requires are described the conversion in [5,7]. of The the establishment location of the user’sof UAI local access GPS to to the the open corresponding database by address; the subseq it thenuent compares system theonly appropriate requires the open conversion database of for the the keywordslocation of of the the user’s user’s local current GPS event,to the andcorresponding then converts address; the corresponding it then compares map the display appropriate according open to thedatabase data intercepted for the keywords in the “address” of the user’s field tocurrent reach theevent, research and goalthen ofconverts the aforementioned the corresponding value-added map location-baseddisplay according mobile to the information data intercepted service. in Furthermore, the “address” the field back-end to reach system the research must first goal build of the the ontologyaforementioned of the correspondingvalue-added location-based event (as shown mobile in information Figure1c) and service. link itFurthermore, to the appropriate the back-end open databases.system must The first APIs build corresponding the ontology toof thethe mapcorrespon are accessedding event on (as the shown basis of in the Figure open 1c) database and link query, it to andthe theappropriate query results open are databases. then developed The APIs to correspond complete theing establishmentto the map are of accessed the whole on UAI-basedthe basis of LODthe open database query, and the query results are then developed to complete the establishment of the Appl. Sci. 2019, 9, 4385 8 of 24

Appl. Sci. 2019, 9, x FOR PEER REVIEW 8 of 23 databases for achieving the goal of fast processing on the basis of the concept of exchanging space whole UAI-based LOD databases for achieving the goal of fast processing on the basis of the concept for time. of exchanging space for time. Regarding the establishment of the UAI-based LOD cloud database, first, the back-end cloud Regarding the establishment of the UAI-based LOD cloud database, first, the back-end cloud database uses the Parse cloud database to process the JSON (JavaScript object notation) format, database uses the Parse cloud database to process the JSON (JavaScript object notation) format, and and then uses a Google Cloud Spreadsheet to process the CSV (comma-separated values) format. then uses a Google Cloud Spreadsheet to process the CSV (comma-separated values) format. By By referring to the aforementioned UAI technology, it processes files or other formats via Dropbox to referring to the aforementioned UAI technology, it processes files or other formats via Dropbox to collatecollate the the relevant relevant government government open open data, data, and and access access system-related system-related images images and and data. data. In In general, general, the governmentthe government open open database database provides provides two two kinds kinds of data of data link link concatenation concatenation methods; methods; one one is thatis that the APIthe linksAPI links provided provided by the by governmentthe government database database are directlyare directly used, used, and and the the other other is to is downloadto download the datathe data for use for by use oneself. by oneself. The reasonThe reason why thiswhy study this study did not did choose not choose the former the former method, method, which which seems moreseems direct more and direct convenient, and convenient, is that the is amountthat the ofamount data used of data by theused system by the is system quite large is quite and large its content and isits more content complicated. is more complicated. If the open If database the open is directlydatabase connected is directly inconnected series, it in will series, only it increase will only the burdenincrease of the interpretation burden of interpretation and management and manageme of the system.nt of Therefore,the system. this Therefore, system this adopted system the adopted method ofthe periodically method of uploadingperiodically open uploading data to open the Dropbox data to the cloud Dropbox database, cloud which database, facilitates which the facilitates management the ofmanagement data categories. of data However, categories. as Dropbox However, has as notDropbox yet provided has not theyet relevant provided API the for relevant JavaScript, API for this systemJavaScript, and Dropboxthis system cannot andbe Dropbox concatenated cannot directly; be concatenated therefore, thedirectly; above therefore, UAI technology the above was UAI used totechnology solve this was problem. used to solve this problem. ThisThis study study established established a a related related travel travel event event ontologyontologyarchitecture architecture(as (asshown shownin inFigure Figure1 c)1c) with with a back-enda back-end LOD LOD cloud cloud database, database, allowing allowing the breadth the breadth and depth and ofdepth the systemof the tosystem be changed to be accordingchanged toaccording the needs to of the the needs system. of the In other system. words, In other the information words, the information breadth can breadth be adjusted can bybe increasingadjusted by or decreasingincreasing events,or decreasing while informationevents, while depth information can be depth determined can be bydetermined matching by it withmatching the provision it with the of back-endprovision data of back-end and functions. data and The functions. correspondence The correspondence between some between events andsome back-end events and data back-end is shown indata Figure is shown2. This in not Figure only 2. makes This not it easyonly formakes the systemit easy for to achievethe system the to research achieve and the developmentresearch and goaldevelopment of flexibly goal tuning of flexibly the depth tuning and the breadth depth ofand recommended breadth of recommended information, information, but also enables but also the eventenables ontology, the event as establishedontology, as by established the system, by toth providee system, block to provide information block connectioninformation accordingconnection to diaccordingfferent event to different situations, event thus situations, improving thus the system’simproving access the system’s efficiency access and response efficiency time. and The response system capturestime. The the system user’s captures position the through user’s position GPS, combines through theGPS, corresponding combines the corresponding government link government open data storedlink open in the data back-end stored in cloud the back-end database cloud with databa the relevantse with event the relevant corresponding event corresponding ontology, and ontology, captures andand comparescaptures and the appropriatecompares the open appropriate information open ininformation the administrative in the administrative area, meaning area, the meaning location ofthe the location corresponding of the corresponding user, in order user, to in provide order to corresponding provide corresponding location-based location-based information information services suchservices as community such as community sharing, parkingsharing, lot parking details, lot emergency details, emergency dialing, and dialing, other and value-added other value-added functions. Finally,functions. using Finally, Google’s using reverse Google’s geocoding reverse function, geocoding the correspondingfunction, the corresponding LOD cloud database LOD cancloud be easilydatabase accessed can be by easily GPS informationaccessed by GPS retrieval informatio to supportn retrieval the subsequent to support application the subsequent of the application system for eofffectively the system and for effi effectivelyciently achieving and efficiently the concept achieving of exchanging the concept space of exchanging for time. space for time.

Corresponding User Event Position

Life Emergency Event Event

Restaurant Parking Lot Police Station Fire Department Position Position Position Position

Parking Lot Police Station Fire Department Restaurant Website Community sharing Urgent call Urgent call Detailed Information Detailed information Detailed Information (a)

Figure 2. Cont. Appl. Sci. 2019, 9, 4385 9 of 24 Appl. Sci. 2019, 9, x FOR PEER REVIEW 9 of 23

Info Location

Fire Police Contact Police Department Station

(b)

FigureFigure 2. 2.Correspondence Correspondence betweenbetween somesome eventsevents and back-end data: ( (aa)) ontology ontology architecture architecture diagram diagram ofof some some tourism-related tourism-related events; events; and and (b) linked(b) linked open open data (LOD)-linkeddata (LOD)-linked diagram diagram (police (police and ﬁre and service). fire 3.3. Architectureservice). of WIAS

3.3. TheArchitecture design conceptof WIAS of this WIAS is a service-oriented architecture (SOA) based on web services, that is, providing relevant information services through the existing network environment and The design concept of this WIAS is a service-oriented architecture (SOA) based on web services, communicating with each other through XML in JSON format. When the required information services that is, providing relevant information services through the existing network environment and are finally assembled and placed on the network for use, it improves the system’s performance communicating with each other through XML in JSON format. When the required information and development difficulties. The concept of this web service design is to plan related cloud web services are finally assembled and placed on the network for use, it improves the system’s services on the basis of the services required by CEOntoIAS. The integration includes the web services performance and development difficulties. The concept of this web service design is to plan related requiredcloud web by theservices interface on the agent basis (IA), of the the services data mining required agent by (DMA), CEOntoIAS. and the The case-based integration reasoning includes agent the (CBRA). These factors are responsible for providing cloud data transfer and system calculation for the web services required by the interface agent (IA), the data mining agent (DMA), and the case-based corresponding agent system, including, offer common cloud information required by the system to accessreasoning core ofagent this ( WebCBRA service—cloud). These factors system are responsible database—which for providing is the aforementioned cloud data transfer SQL and software system IC conceptcalculation that for binds the dicorrespondingfferent access agent parameters system, throughincluding, SQL offer access common templates cloud andinformation is responsible required for communicatingby the system with to relevantaccess core cloud of web this services Web andservice—cloud corresponding system cloud database—which databases to provide is the the correspondingaforementioned cloud SQL informationsoftware IC concept service. that With bind thes support different of access the tourism parame ontologyters through index SQL structure access (seetemplates Figure1 and for is details), responsible after establishingfor communicating the cloud with information relevant cloud association web services graph, and the corresponding corresponding cloudcloud data databases transfer to mechanism provide the must corresponding still be designed, cloud including information the service. client or With user sidethe support (i.e., each of agent the system),tourism servo ontology side (i.e.,index WIAS), structure API (see information Figure 1 transfer for details), interface, after andestablishing cloud information the cloud coreinformation database, whichassociation constitute graph, a cloudthe corresponding information cloud transmission data tran mechanism.sfer mechanism In other must words, still be designed, each agent including program canthe trigger client anor eventuser side (such (i.e., as travel)each agent by calling system), the correspondingservo side (i.e., cloud WIAS), system APIfunction information library transfer (APIs) withinterface, different and parameters cloud information in order tocore obtain database, the corresponding which constitute information a cloud functions information for easily transmission building propermechanism. information In other services, words, aseach detailed agent inprogram [11]. can trigger an event (such as travel) by calling the correspondingFigure3 shows cloud the system WIAS function architecture. library When (APIs) the with source different information parameters is an in access order command, to obtain the correspondingcorresponding SQL information access template functions is for triggered easily buil directlyding throughproper information the web service-based services, as interfacedetailed in by [11]. using the SQL IC constructor. After binding the relevant access parameters, the corresponding access Figure 3 shows the WIAS architecture. When the source information is an access command, the results are retrieved from the raw database, and then, transmitted back to the interface agent system corresponding SQL access template is triggered directly through the web service-based interface by through the web service-based interface. Regardless of whether the source information is a cloud using the SQL IC constructor. After binding the relevant access parameters, the corresponding access default solution, it will also be sent through the web service-based interface to query the predefined results are retrieved from the raw database, and then, transmitted back to the interface agent system rule base, and then return the corresponding cloud default solution to the interface agent. However, through the web service-based interface. Regardless of whether the source information is a cloud if there is no default solution, it will trigger OntoIAS (ontological information agent shell) [10] to default solution, it will also be sent through the web service-based interface to query the predefined directly seek the appropriate cloud recommendation information solution from outside the Internet rule base, and then return the corresponding cloud default solution to the interface agent. However, through the technologies of information search, information retrieval, information classification, and if there is no default solution, it will trigger OntoIAS (ontological information agent shell) [10] to information presentation or sorting. In addition, the raw database provides all frequently historical directly seek the appropriate cloud recommendation information solution from outside the Internet information to OntoCBRA (ontological case-based reasoning agent) [31] as cloud information material through the technologies of information search, information retrieval, information classification, and for case generation, and infrequently historical information with a two-stage time series exploration information presentation or sorting. In addition, the raw database provides all frequently historical algorithm,information as theto informationOntoCBRA material(ontological that case-based triggers OntoDMA reasoning (ontological agent) [31] data asmining cloud agent)information [32] to producematerial cloud for case prediction generation, solutions, and whereinfrequently the construction historical ofinformation predefined with rules a is two-stage under the time supervision series ofexploration domain experts. algorithm, The as aforementioned the information cloud material recommendation that triggers OntoDMA information (ontological solution is data determined mining throughagent) [32] rule to maker, produce and cloud the prediction agent system solutions, architecture where appliesthe construction information of predefined transfer on rules the is Internet under Appl. Sci. 2019, 9, x FOR PEER REVIEW 10 of 23 Appl. Sci. 2019, 9, 4385 10 of 24

the supervision of domain experts. The aforementioned cloud recommendation information solution throughis determined cloud computing through rule technology maker, and for the the agent implementation system architecture of an on-line applies web information service interface, transfer which on enablesthe Internet agent through programs cloud to call computing a common technology function fo library.r the implementation An overview of of some an on-line cloud web web service services’ APIsinterface, is shown which in enables Table1. agent The descriptionprograms to speciﬁc call a common to UAI includesfunction thelibrary. four An categories overview of of connect, some compute,cloud web search, services’ and APIs storage is shown [33], which in Table makes 1. The it easy description to communicate specific withto UAI cloud includes databases, the four and thecategories user can of easily connect, add compute, and update search, related and cloudstorag informatione [33], which functions,makes it easy and to timely communicate reﬂect them with in thecloud corresponding databases, and agent the programs, user can thuseasily achieving add and theupdate research related purpose cloud ofinformation a cloud information functions, agentand system-basedtimely reflect onthem web in service the corresponding technology. agent programs, thus achieving the research purpose of a cloud information agent system-based on web service technology.

Figure 3. WIAS (web service-based information agent system) architecture. Ubi-IA: ubiquitous interface agent,Figure SQL 3. IC:WIAS structured (web service-based query language information integrated circuits,agent system) OntoDMA: architecture. ontological Ubi-IA: data miningubiquitous agent, OntoCBRA:interface agent, ontological SQL IC: Case-Based structured Reasoningquery language (CBR) integrated agent, and circuits, WWW: OntoDMA: World Wide ontological Web. data mining agent, OntoCBRA: ontological Case-Based Reasoning (CBR) agent, and WWW: World Wide TableWeb.1. Overview of some parts of APIs’ (application programming interface) functions. Case-Based Reasoning (CBR). Table 1. Overview of some parts of APIs’ (application programming interface) functions. Case-Based SERVICE SERVICE TO WHOM ReasoningNAME (CBR). DESCRIPTION

CBR_InsTmpCaseDataSERVICE Adds temporarySERVICE data for solutions CBR Agent CBR_Solutions Solution to case library CBRTO Agent WHOM DB_InsCaseBaseNAME AddsDESCRIPTION case base rule Sharing CBR_InsTmpCaseDataDB_InsPrediction Adds Adds temporar a predictiony data rule for solutions Sharing CBR Agent CBR_SolutionsDB_UptCaseBase RecordsSolution whether itto been case converted library Sharing CBR Agent DB_InsCaseBaseDB_UptRawData Records whether Adds case it been base converted rule Sharing Sharing DM_Solutions Solution to prediction rules Data Mining Agent DM_TransCaseToPredDB_InsPrediction Converts from a semanticAdds case a libraryprediction to a prediction rule rule library Data Mining Sharing Agent DB_UptCaseBaseDM_ViewCaseBase Records Views whethe caser it base been converted Data Mining Sharing Agent DB_UptRawDataIA_InsRawData Records Adds whether original it databeen converted Interface Sharing Agent IA_Solutions Solution to redefined rules InterfaceData AgentMining Share_DelUniqueTablesDM_Solutions Deletes the Solution data in each to independentprediction rules data table Sharing Share_IsNumeric Determines whether str NUMBER is a number SharingAgent Share_ViewCBRConverts from Views a semantic case library case summary library to a prediction rule Data Sharing Mining DM_TransCaseToPred Share_ViewDBSDT Views systemlibrary time SharingAgent UAI_ConnectCrossDomain Connects cross-domain Open Data Base UAI_ConnectData Mining DM_ViewCaseBaseUAI_ConnectAlertReg LinksViews registration case base tips UAI_Connect UAI_ConnectRuleSetting Connects rule settings UAI_ConnectAgent UAI_ComputeFileAccessIA_InsRawData File Addsaccess computationoriginal data UAI_Compute Interface Agent UAI_ComputeTimeSeriesAccessIA_Solutions TimeSolution series to computation redefined rules UAI_Compute Interface Agent UAI_ComputeTriggerConfigShare_DelUniqueTables Deletes Triggers the data configuration in each independent computation data table UAI_Compute Sharing UAI_SearchTimeSeries Time series search UAI_Search UAI_SearchBuildIndexFieldsShare_IsNumeric Determines Creates whether an indexstr NUMBER field is a number UAI_Search Sharing UAI_SearchStatisticsShare_ViewCBR Views Searches case library statistics summary UAI_Search Sharing Share_ViewDBSDTUAI_StorageFFStatus Folder Views and system file status time UAI_Storage Sharing UAI_ConnectCrossDomainUAI_StorageFFManipulation Connects Folder cross-do and filemain operation Open Data Base UAI_Storage UAI_Connect UAI_StorageFTransimission Folder and file transmission and status UAI_Storage UAI_ConnectAlertReg Links registration tips UAI_Connect UAI_StorageFFSharing Folder and file sharing and integration UAI_Storage Appl. Sci. 2019, 9, 4385 11 of 24

3.4. Design and Construction of Dr. What-Info System with CEOntoIAS The tourism-related ontology (Figure1) technology can help users quickly, accurately, and effectively obtain appropriate and timely cloud information and use the OntoIAS for allowing us to accomplish the research purpose of expanding the information agent shell of EOntoIAS to CEOontoIAS (refer to Figure4 for details). The support includes: (1) driving the intelligent cloud information data mining agent system OntoDMA [8] to utilize the classification and clustering technology of cloud data mining; extracting the relevant cloud information operation knowledge or rules according to the relevant support and confidence, as provided in advance by the system cloud operation mode; and actively providing the system with suitable, real-time, and fast cloud information prediction service; (2) driving the intelligent cloud information case reasoning agent system OntoCBRA [31] to match the cloud information ontology service and similarity calculation between the corresponding cases, introducing a case-based reasoning mechanism, actively learning the relevant system knowledge, and expanding the cloud system knowledge and operation mode, and thus enhancing system-related cloud information processing robustness; (3) when neither of the aforementioned factors can provide an appropriate cloud information solution, the system triggers OntoIAS [10] through the solution finder to search for the appropriate cloud information solution from outside the Internet directly through information search, information retrieval, information classification, information presentation or sorting, and other technologies. This solution is passed to the front- and back-related cloud information processing systems via Ubi-IA (ubiquitous interface agent) with a canonical user request representation language (CURRL) [10] in JSON format through the solution finder, becoming the basic cloud information material for OntoDMA and OntoCBRA to further expand learning prediction and case-based reasoning, which completes the learning cycle of the whole system in response to the information query and enhances the robustness of system cloud information processing and decision-making support. The CURRL is a frame-based command representation that makes it easy to map query command intentions, objects, and goals into corresponding frame slots. Table2 shows the frame slots and their descriptions, whereas Table3 illustrates some examples of query commands. Finally, through the cloud information processing of OntoDMA, OntoCBRA, and OntoIAS three-stage intelligent decision-making, as well as with the support of the cloud intelligent multi-agent system shell CEOntoIAS (which supports the processing, exchange, communication, operation, integration, analysis, and decision–making of cloud information), the research goal of autonomously searching and recommendingAppl. Sci. 2019, 9, x FOR the bestPEERcloud REVIEW information solution can easily be reached. 12 of 23

WWW

OntoIAS

(3) OntoCrawler Ontology Databases

(1) Indexing OntoDMA OntoExtractor Dr. What-Info Ubi- APPs Interface Solution Agent Finder Ontological (IA) Database OntoCBRA OntoClassifier (2)

OntoRecommander User Profile Databases

CEOntoIAS: WIAS-supported cloud Platform

Figure 4. Dr. What-Info system architecture.

Table 2. Frame structure of canonical user request representation language (CURRL).

SLOTS DESCRIPTION Theme Topic of user’s command aTheme Description of topic tTime Related time of topic tSpace Related space of topic object Topic objects oIdentity Object description oCardinality Number of objects oTime Related time of object oSpace Related space of object

Table 3. Examples of query commands with CURRL.

QUERY COMMAND CURRL Query [Theme = +SJU, What is St. John’s University (SJU), Taiwan? aTheme = Taiwan, tSpace = At (WWW)] Command [ Theme = +Anymore, Anything else? tTime = Now, object = Related, oSpace = At (Last-one) ConditionalCommand [ Condition [Theme = −N.Y. SJU, tTime = Now, Retrieving Taiwan SJU webpages with the exception of New York SJU tSpace = At (WWW)], Command [Theme = +Taiwan SJU, tTime = Now, tSPace = At (WWW)]]

Appl. Sci. 2019, 9, 4385 12 of 24

Table 2. Frame structure of canonical user request representation language (CURRL).

Slots Description Theme Topic of user’s command aTheme Description of topic tTime Related time of topic tSpace Related space of topic object Topic objects oIdentity Object description oCardinality Number of objects oTime Related time of object oSpace Related space of object

Table 3. Examples of query commands with CURRL.

QUERY COMMAND CURRL Query [Theme = +SJU, What is St. John’s University (SJU), Taiwan? aTheme = Taiwan, tSpace = At (WWW)] Command [ Theme = +Anymore, Anything else? tTime = Now, object = Related, oSpace = At (Last-one) ConditionalCommand [ Condition [Theme = N.Y. SJU, − tTime = Now, Retrieving Taiwan SJU webpages with the tSpace = At (WWW)], exception of New York SJU Command [Theme = +Taiwan SJU, tTime = Now, tSPace = At (WWW)]]

3.5. Establishment of System Front-End APP This system APP used EzoAPP (https://ezoui.com/app/) as its main development tool. However, with wearable devices (such as SNOY SWR 50 in Android Wear), Android Studio is used for development, including using a Sony SWR 50 acceleration sensor to calculate the number of steps, a graphical display of users’ basic data to calculate the total calories burned on the day, and other corresponding information services. When the user executes this system APP, the system will automatically compare the current GPS position with the back-end server address to provide the administrative area information corresponding to the user’s address and start the system APP operation process: (1) When the user selects the corresponding travel event, as shown in Figure1c, the system APP converts the obtained longitude and latitude data into a corresponding complete physical address through the geocoder API via reverse geocoding [6]. (2) The system APP cuts out the keywords of the administrative area of the three-tier address. (3) The system APP sends the keywords with the CURRL in JSON format to the cloud server CEOntoIAS, and then the UAI-based LOD access technology is used to return the corresponding LOD data with the support of the aforementioned three-stage intelligent decision-making. (4) The system APP interface displays the corresponding information about the open links in the administrative areas (e.g., information about restaurants and parking lots related to “food” in general events, information about sports centers or places in the “entertainment” section, information about police and ﬁre control in emergencies, information about medical institutions in disease or accidents). (5) When the user selects the interested information option, the detailed information (e.g., restaurant oﬃcial website) will be displayed. If the user likes its content, it can be directly shared with Facebook and feedback recommendation information to enhance the robustness of LBS information in the proposed system. Currently, the front end of the proposed system includes three subagents: personal health information monitoring and consulting Appl. Sci. 2019, 9, x FOR PEER REVIEW 13 of 23

3.5. Establishment of System Front-End APP This system APP used EzoAPP (https://ezoui.com/app/) as its main development tool. However, with wearable devices (such as SNOY SWR 50 in Android Wear), Android Studio is used for development, including using a Sony SWR 50 acceleration sensor to calculate the number of steps, a graphical display of users’ basic data to calculate the total calories burned on the day, and other corresponding information services. When the user executes this system APP, the system will automatically compare the current GPS position with the back-end server address to provide the administrative area information corresponding to the user’s address and start the system APP operation process: (1) When the user selects the corresponding travel event, as shown in Figure 1c, the system APP converts the obtained longitude and latitude data into a corresponding complete physical address through the geocoder API via reverse geocoding [6]. (2) The system APP cuts out the keywords of the administrative area of the three-tier address. (3) The system APP sends the keywords with the CURRL in JSON format to the cloud server CEOntoIAS, and then the UAI-based LOD access technology is used to return the corresponding LOD data with the support of the aforementioned three-stage intelligent decision-making. (4) The system APP interface displays the corresponding information about the open links in the administrative areas (e.g., information about restaurants and parking lots related to “food” in general events, information about sports centers or places in the “entertainment” section, information about police and fire control in emergencies, information about medical institutions in disease or accidents). (5) When the user selects the interested information option, the detailed information (e.g., restaurant official website) will be displayed. If the user likes its content, it can be directly shared with Facebook and feedback recommendation information to enhance the robustness of LBS information in the proposed system. Appl. Sci. 2019, 9, 4385 13 of 24 Currently, the front end of the proposed system includes three subagents: personal health information monitoring and consulting agent, restaurant information consultation and sharing agent, andagent, beverage restaurant safety information consultation consultation agent. Figure and sharing5 illustrates agent, their and in beveragedividual system safety consultation architectures agent. and functions.Figure5 illustrates their individual system architectures and functions.

Bluetooth SONY SWR50 Personal health monitoring and consulting agent

Related Personal Console knowledge information

Knowledge Profile Profile Volume Numerical Sports display display editing drawing display recommendation

(a)

Restaurant information consultation and sharing agent

Mobile agent Cloud server APP

Data Wireless GPS Cloud User interface reception and setting positioning database return

UAI Data parsing Google sheets technology

Appl. Sci. 2019, 9, x FOR PEER REVIEW 14 of 23 (b)

Beverage safety consultation agent

Knowledge Ministry of Health and Data scaning inquiring Welfare official website

Police spot Telephone Knowledge Detailed Detail display details dailing display information (c)

Figure 5. Three subsystem architectures and their functions: (a) personal health information monitoring Figure 5. Three subsystem architectures and their functions: (a) personal health information and consulting agent; (b) restaurant information consultation and sharing agent; and (c) beverage monitoring and consulting agent; (b) restaurant information consultation and sharing agent; and (c) safety consultation agent. GPS: global positioning system, UAI: universal application interface, beverage safety consultation agent. GPS: global positioning system, UAI: universal application APP: Application. interface, APP: Application. 4. Evaluation of System Presentation and Eﬃcacy 4. Evaluation of System Presentation and Efficacy 4.1. System Presentation 4.1. System Presentation As mentioned above, the proposed system includes three basic and important tourism-related agents:As mentioned the PHI agent, above, the the RI proposed agent, andsystem the incl BSudes agent. three On basic the basisand important of the development tourism-related and practiceagents: the of thePHI Dr. agent, What-Info the RI agent, system, and this the proposed BS agent. system On the is expectedbasis of the to development develop from and the relevantpractice practicalof the Dr. technologies What-Info system, of mobile this devicesproposed with system the support is expected of the to powerfuldevelop from information the relevant agent practical system technologiesCEOntoIAS, atof themobile back end,devices by referringwith the to support the UAI-based of the LODpowerful access information technology, agent as mentioned system CEOntoIAS,above in collaboration at the back with end, GPS by locationreferring interception, to the UAI-based and with LOD the access support technology, of appropriate as mentioned link open aboveinformation in collaboration and domain with ontology, GPS location in order interception, to compare and and with intercept the support appropriate of appropriate location link service open information and domain ontology, in order to compare and intercept appropriate location service information to effectively improve the quality of the system’s mobile information regarding monitoring and recommendation. The PHI agent [34] is used by Sony SWR50 to transfer the users’ walking steps back to the system front-end APP, and then graphically display the information through the APP. The current system can display the number of steps taken in the last seven days, the average number of steps per day, the amount of calories burned today, and the number of steps taken. When the user’s activity on the day is lower than the target value, the user can click the “Recommendation” button, and then, click “Related Knowledge” and “Personal Data” to switch to the selected page for the corresponding information service, as shown in Figure 6. The information about quality restaurants and their corresponding parking lots, as recommended by the RI agent [35], is based on St. John’s University GPS, as shown in Figure 7a,b, respectively. The corresponding webpage of the click on each line of the lower part of the above webpage is shown in Figure 7c, through which users can obtain all relevant information in advance. Finally, if users choose a destination, the system will automatically navigate and present the destination, as shown in Figure 7d. All the above bulk information recommendations are the greatest contributions of the extended exploration of this study.

The average number Related system of steps per day (平均 operation buttons, 步數); including, Graphically display 1. Recommendation The recommendation the number of steps (建議) screen will indicate the current location of the taken in the last 2. Console (控制台) user and capture the seven days; 3. Related The amount of closest sports location Knowledge (相關 calories burned today from the system 知識 (今日消耗熱量); ) database. The number of steps 4. Personal Data (個

Appl. Sci. 2019, 9, x FOR PEER REVIEW 14 of 23

Beverage safety consultation agent

Knowledge Ministry of Health and Data scaning inquiring Welfare official website

Police spot Telephone Knowledge Detailed Detail display details dailing display information (c)

Figure 5. Three subsystem architectures and their functions: (a) personal health information monitoring and consulting agent; (b) restaurant information consultation and sharing agent; and (c) beverage safety consultation agent. GPS: global positioning system, UAI: universal application interface, APP: Application.

Appl. Sci. 2019, 9, 4385 14 of 24 4. Evaluation of System Presentation and Efficacy information4.1. System toPresentation eﬀectively improve the quality of the system’s mobile information regarding monitoring and recommendation.As mentioned above, the proposed system includes three basic and important tourism-related agents:The PHIthe PHI agent agent, [34] the is used RI agent, by Sony and SWR50 the BS toagent. transfer On the the basis users’ of walkingthe development steps back and to thepractice system front-endof the Dr. APP, What-Info and then system, graphically this proposed display system the information is expected through to develop the APP.from the The relevant current practical system can displaytechnologies the number of mobile of steps devices taken with in the the last support seven days,of the the powerful average numberinformation of steps agent per system day, the amountCEOntoIAS, of calories at the burned back end, today, by and referring the number to the ofUAI-based steps taken. LOD When access the technology, user’s activity as mentioned on the day is lowerabove than in collaboration the target value, with the GPS user location can click interception, the “Recommendation” and with the support button, of appropriate and then, click link “Related open Knowledge”information andand “Personaldomain ontology, Data” to in switch order to thecompare selected and page intercept for the appropriate corresponding location information service service,information as shown to effectively in Figure6 . improve the quality of the system’s mobile information regarding monitoringThe information and recommendation. about quality restaurants and their corresponding parking lots, as recommended by theThe RI agentinformation [35], is basedabout onquality St. John’s restaurants University and GPS, their as corresponding shown in Figure parking7a,b, respectively. lots, as recommended by the RI agent [35], is based on St. John’s University GPS, as shown in Figure 7a,b, The corresponding webpage of the click on each line of the lower part of the above webpage is shown respectively. The corresponding webpage of the click on each line of the lower part of the above in Figure7c, through which users can obtain all relevant information in advance. Finally, if users webpage is shown in Figure 7c, through which users can obtain all relevant information in advance. choose a destination, the system will automatically navigate and present the destination, as shown Finally, if users choose a destination, the system will automatically navigate and present the in Figure7d. All the above bulk information recommendations are the greatest contributions of the destination, as shown in Figure 7d. All the above bulk information recommendations are the greatest extended exploration of this study. contributions of the extended exploration of this study.

Appl. Sci. 2019, 9, x FOR PEER REVIEW 15 of 23 (a) (b)

On the "Personal Data" page, users can see information such When the user as gender, age, height clicks the "Related and weight. Edit these Knowledge" materials by clicking button, the system gets relevant the "Edit" (編輯) health knowledge button below. You according to the can click the "?" user's click on the button on the right to text. further obtain the corresponding information. (c) (d)

FigureFigure 6. 6.System System screen screen display display ofof thethe personalpersonal health information information (PHI) (PHI) agent: agent: (a) ( asystem) system starting starting screen;screen; (b ()b display) display of of the the recommendation recommendation screenscreen at the the corresponding corresponding Google Google map; map; (c) ( cdisplay) display of the of the relatedrelated knowledge—BMI: knowledge—BMI: Body Body Mass Mass Index;Index; andand (d) display of of the the personal personal data—editing data—editing page. page.

Detailed Information Detailed Information information about quality information about about a parking restaurants about a quality corresponding lot—MacKay based on St. restaurant— parking lots Memorial John’s Big Mac Ice based on St. Hospital University Cream (巨無霸 John’s Tamsui Branch GPS. 冰淇淋). University Parking Lot (馬 GPS. 偕醫院淡水分院 (a) (b) 停車場).

Website An illustration information of Introduction to Destination of optimal a quality a quality navigation (導 routes by drive restaurant (網 restaurant, say, on Google Big Mac Ice 航) to a quality 站介紹)—Big Maps based on Cream, with restaurant— St. John’s Mac Ice Cream the user Big Mac Ice University GPS (巨無霸冰淇淋 feedback Cream (巨無霸 to Big Mac Ice ). interface. Cream. 冰淇淋). (c) (d)

Figure 7. Bulk recommendation by the restaurant information (RI) agent based on St. John’s University GPS: (a) information about quality restaurants; (b) information about the corresponding parking lot; (c) information on the website of Big Mac Ice Cream; and (d) destination navigation and presentation.

As mentioned above, the BS Agent [36] has two kinds of query modes—automatic scanning and manual query, as shown in Figure 8. When the user clicks the “I want to scan” button to enter the scanning page, and then presses the “Scan” button to focus on the desired QR/barcode, the system will display the decoded result on the screen and provide corresponding and appropriate information based on the system database. If the user clicks the query button of the additive, a more detailed information query action can be performed; if the user has further need, they can press the “Help needed” button to seek appropriate emergency assistance. When the user enters the emergency assistance function, the system provides a map of local police and fire stations in the case of New Taipei City, which includes current positioning information. As a result, the users can seek nearby help, and directly dial the phone to make an emergency rescue. In addition, when the user clicks the button of “Manual Query” on the homepage of the system, the user can manually activate the query keyword function. Finally, the official website of the Ministry of Health and Welfare is also provided on the homepage of the system, which is convenient for users to query. Appl. Sci. 2019, 9, x FOR PEER REVIEW 15 of 23

(a) (b)

Figure 6. System screen display of the personal health information (PHI) agent: (a) system starting Appl. Sci.screen;2019, 9 (,b 4385) display of the recommendation screen at the corresponding Google map; (c) display of the 15 of 24 related knowledge—BMI: Body Mass Index; and (d) display of the personal data—editing page.

FigureFigure 7. 7.Bulk Bulk recommendation recommendation by theby restaurantthe restaurant information information (RI) agent(RI) basedagent onbased St. John’son St. University John’s GPS:University (a) information GPS: (a) aboutinformation quality about restaurants; quality restaurants; (b) information (b) information about the correspondingabout the corresponding parking lot; (c)parking information lot; (c on) information the website on of the Big website Mac Ice of Cream;Big Mac and Ice (Cream;d) destination and (d) destination navigation navigation and presentation. and presentation. As mentioned above, the BS Agent [36] has two kinds of query modes—automatic scanning and manualAs query, mentioned as shown above, in the Figure BS Agent8. When [36] has the tw usero kinds clicks of the query “I wantmodes—automatic to scan” button scanning to enter and the scanningmanual page,query, and as thenshown presses in Figure the “Scan”8. When button the user to focusclickson the the “I desiredwant to QRscan”/barcode, buttonthe to enter system the will scanning page, and then presses the “Scan” button to focus on the desired QR/barcode, the system display the decoded result on the screen and provide corresponding and appropriate information based will display the decoded result on the screen and provide corresponding and appropriate on the system database. If the user clicks the query button of the additive, a more detailed information information based on the system database. If the user clicks the query button of the additive, a more query action can be performed; if the user has further need, they can press the “Help needed” button detailed information query action can be performed; if the user has further need, they can press the to seek appropriate emergency assistance. When the user enters the emergency assistance function, the “Help needed” button to seek appropriate emergency assistance. When the user enters the emergency systemassistance provides function, a map the of system local police provides and a ﬁre map stations of local in police the case and of fire New stations Taipei in City, the whichcase of includes New currentTaipei positioningCity, which information.includes current As positioning a result, the information. users can seekAs a nearbyresult, the help, users and can directly seek nearby dial the phonehelp, toand make directly an emergency dial the phone rescue. to make In addition, an emergency when rescue. the user In clicks addition, the buttonwhen the of “Manualuser clicks Query” the onbutton the homepage of “Manual of Query” the system, on the the homepage user can manuallyof the system, activate the user the can query manually keyword activate function. the query Finally, thekeyword oﬃcial function. website ofFinally, the Ministry the official of website Health andof the Welfare Ministry is of also Health provided and Welfare on the is homepage also provided of the system,on the whichhomepage is convenient of the system, for users which to is query. convenient for users to query. Appl. Sci. 2019, 9, 4385 16 of 24

Appl. Sci. 2019, 9, x FOR PEER REVIEW 16 of 23

System homepage: The scanning Clicking the I want to scan ( page displaying additive 我要掃描) the decoded query button result and Manual query ( (所有添加物 providing 查知識查詢 ) appropriate ), a more Official website information and detailed of the Ministry corresponding information of Health and functions. query action Welfare (衛服 can be performed. 部官網).

(a) (b)

The “Help needed” page to seek appropriate Clicking the emergency button of assistance with “Manual Query” GPS. Dial the (查知識) on the phone to make homepage, the an emergency user can rescue (撥打電話 manually active the query 求助). keyword function. (c) (d)

FigureFigure 8. System 8. System screen screen display display of of the the beverage beverage safety (BS) (BS) agent: agent: (a) ( asystem) system homepage homepage and scanning and scanning page;page; (b) the(b) additivethe additive query query page; page; (c ()c) “Help “Help needed”needed” page; page; and and (d ()d “Manual) “Manual Query” Query” page. page.

4.2. System4.2. System Evaluation Evaluation TheThe following following comparisons comparisons and experimentsand experiments invited invited users fromusers related from departmentsrelated departments (department of information(department and of information communication), and communication), unrelated departments unrelated departments (department (department of business of management), business management), and the middle/high age-bracket (users above 45 years of age). There were 10 persons and the middle/high age-bracket (users above 45 years of age). There were 10 persons in each group, in each group, and the 3 groups were used as a reference frame for analysis. All comparisons and and theevaluation 3 groups results were were used based as a on reference opinions frame in whic forh there analysis. was a All75% comparisons or greater agreement and evaluation in the three results weregroups, based onwhich opinions represented in which that the there majority was aof 75%the people or greater were agreementin agreement. in the three groups, which representedThe thatthree the main majority comparison of the objects people of werethe PHI in agreement.agent are similar to personal mobile APPs, such asThe Google three Fit, main Millet comparison Movement objects, and Motion-Pedometer of the PHI agent [34]. aresimilar Table 4 toshows personal the comparison mobile APPs, results such as Googleof the Fit, “basic Millet display” Movement, functions and of Motion-Pedometer the related system. The [34]. corresponding Table4 shows comparison the comparison systems results are not of the “basicmuch display” different functions from the of proposed the related system, system. and Thethe pa correspondingrts of the function comparison display are systems similar. are Table not 5 much diﬀerentillustrates from the proposed“advancedsystem, query” andfunctions the parts between of the the function proposed display system are and similar. the control Table system,5 illustrates showing the main wisdom of the proposed system that can recommend suitable health information the “advanced query” functions between the proposed system and the control system, showing the in a timely and appropriate manner. In summary, the three control APPs are displayed in a bar graph main wisdom of the proposed system that can recommend suitable health information in a timely and when displaying the number of steps in its most recent use. The Millet Movement must be bound to appropriatethe Millet manner. bracelet In tosummary, be applied, the meaning three controlits system APPs cannot are displayedbe paired with in a other bar graph wearable when devices. displaying the numberThe motion-pedometer of steps in its most requires recent a fee use. to activate The Millet other Movement advanced mustfunctions, be bound such as to recommendation the Millet bracelet to be applied,functions. meaning However, its systemthe aforementioned cannot be paired function withs, namely other wearablethe mobile devices. APP proposed The motion-pedometer by the PHI requiresagent, a can fee be to easily activate solved, other which advanced is completely functions, in line such with as the recommendation original intention of functions. this research— However, the aforementionedtimely, appropriate, functions, and free, namely and the the latest mobile and complete APP proposed mobile byinformation the PHI agent, services. can be easily solved, which is completely in line with the original intention of this research—timely, appropriate, and free, and the latest and complete mobile information services. Appl.Appl. Sci.Appl. Sci. 2019 2019Sci. Sci., 9 2019,2019 9x, FORx, ,FOR99, ,x PEER 4385FOR PEER REVIEWPEER REVIEW REVIEW 1717 of of2317 23 of 2317 of 24

TableTableTable 4. 4.Comparison Comparison 4. Comparison results results results of of system system of system basic basic basicfunctions. functions. functions. Table 4. Comparison results of system basic functions. MilletMilletMillet Motion-Motion-Motion- GoogleGoogleGoogle Fit Fit Fit PHIPHI AgentPHI Agent Agent Google Fit MilletMovementMovement MovementMovement Motion-PedometerPedometerPedometerPedometer PHI Agent UserUser Userprofile profile proﬁleprofile ✓XXXX ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ LineLineLine Linechart/bar chart chart/bar /chart/barbar graphgraphgraphgraph

BarBarBar graph Bar graph graph BarBarBar graphBar graph graph BarBar graphBar Bargraph graph

DisplayDisplayDisplay method method methodmethod

QueryQuery the number the number of of QueryQuery Querythe the number number the number of of ✓ ✓ ✓ ✓ ✓XXXX ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ stepsstepsofsteps in steps in the the in inday the theday day Query the calories on QueryQueryQuery Querythe the calories thecalories the calories calories on on on ✓XXXX ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ thethe onday theday the day day ✓ ✘ Legend:Legend:Legend: “✓ “Legend:”✓ means” “ means✓” means “ Xit ”hasit meanshas itthis hasthis itfunction, this hasfunction, thisfunction, function,whereas whereas whereas whereas“✘ “”✘ means” “ means✘ “”x ”means meansit doesit does it it not.does does not. not. not.

TableTableTable 5. 5.Comparison Comparison 5. Comparison results results results of of system system of system advanced advanced advanced functions. functions. functions.

GoogleTableGoogleGoogle 5. Comparison MilletMilletMillet results ofMotion-Motion- systemMotion- advanced functions. PHIPHI AgentPHI Agent Agent FitFit Google Fit Movement FitMovementMovement Millet MovementPedometer PedometerPedometer Motion-Pedometer PHI Agent QueryQueryQuery theQuery the number thenumber the number of of of of ✓✓ ✓XX ✓✓ ✓ ✘✘ ✘ x✘✘ ✘ x stepsstepssteps insteps in the the in inmonth the month monthmonth QueryQueryQueryQuery the the calories the thecalories calories calories in in the in ✓✓ ✓XX ✓✓ ✓ ✘✘ ✘ x✘✘ ✘ x thethe month themonthmonth month RelatedRelatedRelatedRelated knowledge knowledge knowledge ✘✘ ✘x ✘✘ ✘ x✘✘ ✘ x ✓✓ ✓ X UserUser UsergroupUser group group ✘✘ ✘x ✘✘ ✘ x ✓✓ ✓ X ✘✘ ✘ x UserUser UserprofileUser profile profile profile ✘✘ ✘x ✘✘ ✘ x✘✘ ✘ x ✓✓ ✓ X InformInformInform the theInform user theuser ofuser the of the user the of of the the closest Recommendation No NoPaidPaid Paidto to to Paid to activate sports venue on the basis of RecommendationRecommendationRecommendation NoNo No No No No closestclosestclosest sports sports sports venue venue venue on on the theon the activateactivateactivate location of the user basis basisof location of location of the of user the user Legend: “X” means it has this function; whereas “x”basis meansbasis of itof location does location not. of of the the user user Legend:Legend:Legend: “✓ “”✓ means” “ means✓” means it hasit has itthis hasthis function; this function; function; whereas whereas whereas “✘ “”✘ means” “ means✘” means it doesit does it not.does not. not. The RI agent effectiveness experiment first researched the system’s effectiveness and various TheThe RIThe RI agent agentRI agent effectiveness effectiveness effectiveness experiment experiment experiment first first resefirst researched researchedarched the the system’s thesystem’s system’s effectiveness effectiveness effectiveness and and variousand various various function tests. This study used the comparison table for LBSs, as proposed by Tu [37], and classified the functionfunctionfunction tests. tests. tests. This This studyThis study study used used usedthe the comparison thecomparison comparison table table tablefor for LBSs, forLBSs, LBSs, as as proposed proposedas proposed by by Tu Tuby [37], Tu[37], and[37], and classifiedand classified classified thethe proposed theproposedproposed proposed 12 1212 items itemsitems12 items into intointo locationinto locationlocation location positioning, positioning,positioning, positioning, hardware hardwarehardware hardware device, device,device, device, application applicationapplication application management, management, management, and and and and system systemsystemsystempresentation presentation presentation presentation for for comparisonfor comparison forcomparison comparison [35 ].[35]. This[35]. [35].This experimentThis Thisexperiment experiment experiment indicated indicated indicated indicated that that athat sound thata asound sound informationa sound information information information recommender recommenderrecommenderrecommendersystem must system system conformsystem must must must toconform conform more conform verificationto to more moreto more verifi verifi conditions; verificationcationcation conditions; conditions; therefore, conditions; therefore, thetherefore, therefore, information the the information theinformation recommendationinformation recommendationrecommendationrecommendationand value added and and valueand servicevalue value added added classes added service service were service classes addedclasses classes were towere the wereadded added comparison added to to the theto comparison thecomparison table, comparison as shown table, table, table, as in as shown Table shownas shown6 .in Thein in object TableTableTablesystem 6. 6.The The6. compared objectThe object object system system in system this compared compared experiment compared in in this was thisin experiment thethis experiment LBSexperiment system was was developedthewas the LBS theLBS systemLBS bysystem thesystem developed government—Taiwan developed developed by by the bythe the Food government—Taiwangovernment—Taiwangovernment—TaiwanMap (http://www.foodmap.tw Food Food Food Map Map Map(http://www.foodmap. (http://www.foodmap./ FM.html(http://www.foodmap.). As thetw/FM.html). systemtw/FM.html).tw/FM.html). quality As As matchesthe theAs sy thesystemstem sy this stemquality quality system, quality matches matches it matches was selected thisthis system,thisas system, the system, controlit itwas was it selectedwas group. selected selected Theas as the overalltheas control thecontrol control function’s group. group. group. The The up-to-standard overallThe overall overall function’s function’s function’s rate up-t of up-t thiso-standard up-to-standard systemo-standard rate was rate of 87.5%,rate of this thisof this whereas systemsystemsystemthe was up-to-standardwas 87.5%,was 87.5%, 87.5%, whereas whereas whereas rate the ofthe up-to-standard the theup-to-standard Taiwanup-to-standard Food rate rate Map, ofrate of the the asof Taiwan developed theTaiwan Taiwan Food Food byFood Map, Map, the Map, government,as as developed developedas developed by was by the onlybythe the 43.7%. The reasons were that (1) this system uses location information efficiently to provide a more accurate Appl. Sci. 2019, 9, 4385 18 of 24 information service, and (2) with the support of LOD (linked open data), this system architecture uses the back-end database more efficiently and provides users with more all-inclusive LBS.

Table 6. System function comparison analysis.

Class Item RI Agent Taiwan Food Map Indoor positioning X x Location positioning Outdoor positioning X x RFID storage x x Hardware device Environmental sensing device x x DBMS XX Management application XX Application management Mobile app X x Data transmission XX Easy update XX System presentation Ease of implementation XX Actual display XX Location based service (LBS) XX Information recommendation Community sharing X x Satisfaction X x Emergency call X x Value added service Navigation X x Legend: “X” means that it has this function; whereas “x” means it does not.

Regarding the comparison of the BS agent functions [36], the authors selected the iQC Commodity Safety Information website app (http://iqc.com.tw/). This app provides free use of commodity barcodes for the public to check whether a product has passed inspection, and provides qualified information. The iQC can also manually specify the type of product to be queried through keywords. The important thing is that the source information of the product is the open data of the open data platform of the Health and Welfare Department, Taiwan, which is also used as a source of data through cloud link technology, and regularly updates its data content regarding products. Summarizing the above similarities, iQC is used as a comparison system to verify the pros and cons of the proposed system. Furthermore, it is necessary to evaluate the overall usability of the two-system interface design, especially the usability of the interface content and the ease of use of system operation. The analysis of the former is based on the International Organization for Standardization (ISO) 9241; according to its definition, three dimensions—effectiveness, efficiency, and satisfaction are used to evaluate usability. Whitney Quesenbery (http://www.wqusability.com/) suggested using the “5E” function, which measures usability. Regarding the ease of using the system, Jakob Nielsen (http://www.useit.com/jakob/) strongly recommended 10 basic principles for user interface design. The relevant evaluation parameters and the analysis results are shown in Table7. There are 15 items in the overall interface satisfaction, which are calculated as 7 points for each item, where full satisfaction is 100 points, over 100 points for calculating as 100 points. The satisfaction score of the proposed system interface design is 84 points, whereas the iQC App score is 56 points. The main differences between the two systems are (1) the system takes advantage of considering the humanized guidance design concept, which directly enhances the visibility of the system in terms of affinity, accessibility, transparency of system status, and avoidance of errors, and allows users to identify the extent of their work without back-tracking, and (2) both systems have room for improvement in two aspects: error tolerance and help documents. Appl. Sci. 2019, 9, 4385 19 of 24

Table 7. Analysis of overall satisfaction with interface design of the two systems.

Standard Items BS Agent iQC App Efficient XX Effective XX Usability Quesenbery5E Engaging X x Error tolerant x x Easy to learn XX Visibility of system status X x Match between system and the XX real world User control and freedom XX Easy to use Nielsen Consistency and standards XX Error prevention X x Recognition rather than recall X x Flexibility and efficiency of use XX Aesthetic and minimalist design XX Help users recognize, diagnose, x x and recover from errors Help and documentation x x Total satisfaction score 84 56 Legend: “X” means it has this function; whereas “x” means it does not.

The design preference to importance ratio (DIR) (Equation (1)), as proposed by Ha [38], was taken as the design principle of the human–machine interface (HMI) of this system, as it is perfect for combining with the balancing index (BI) (see Equation (2)) to deﬁne the interface. In other words, if BI is zero, all the HMI elements in this design are balanced and perfect. Its physical meaning is that the HMI design meets the principle of the design preference for importance, and that users’ operation of the system is more consistent with user demand:

DPij Pn i=1 DPij DIRijk = , (1) Iik Pn i=1 Iik

Pn i=1 log10 DIRijk BI = , (2) jk n where DIRijk refers to the DIR of design attribute j and importance attribute k of HMI interface element i. DPij indicates the design preference of design attribute j of HMI interface element i. Iik represents design importance k of the importance attribute of the HMI interface element i. BIjk refers to the balance index of design attribute j and importance attribute k, whereas n indicates the total number of HMI interface elements. A five-scale evaluation scheme was utilized to evaluate each design preference (DPij). In other words, the design preferences included very good, good, moderate, weak, and very weak, and their corresponding values were 5, 4, 3, 2, and 1, respectively. Table8 illustrates the evaluation of the HMI elements in our design attributes, as well as their corresponding informational importance of proposed agents, as evaluated using the analytic hierarchy process [38], whereas Table9 shows the evaluation results of DIR with average BI = 0.008430 (BI should approach zero for the best balance) of the proposed agents. “A little bit needs to improve” means that the proposed system interface Appl. Sci. 2019, 9, 4385 20 of 24 design must be slightly adjusted but will not affect the operations of the whole system. The verification results showed that the human–machine interface of our proposed agents can meet important design preferences and provide approximately optimal balance.

Table 8. Evaluation of human–machine interface (HMI) elements and their corresponding importance of proposed agents.

Informational Agent HMI Elements Description Design Preference Remarks Importance Recommendation RDBOT 5 0.192308 Bottom in text Bottom Console CBOT 3 0.269231 Bottom in text PHI Bottom Agent Related RKBOT Knowledge 3 0.269231 Bottom in text Bottom Personal Data PDBOT 3 0.269231 Bottom in text Bottom GMAP Google Map 5 0.166667 Graphical display GPS GPS Location 3 0.233333 Label in text Recommendation RI RDBOT 3 0.233333 Bottom in text Bottoms Agent NAVI Navigation 3 0.183333 Bottom in text Sharing by FB 3 0.183333 Label in text Facebook Scanning SBOT 5 0.263158 Bottom in text BS Bottom Agent QBOT Query Bottom 3 0.368421 Bottom in text WBOT Website Bottom 3 0.368421 Bottom in text

Table 9. Evaluation results of design preference to importance ratio (DIR) with average balancing index (BI) = 0.008430 of proposed agents.

Agent HMI Elements DIR Description BI Average BI RDBOT 1.303242 A little bit needs to improve PHI CBOT 0.926256 A little bit needs to improve 0.016884 Agent RKBOT 0.930887 A little bit needs to improve PDBOT 0.926256 A little bit needs to improve GMAP 1.266055 A little bit needs to improve GPS 0.904325 A little bit needs to improve 0.008430 RI RDBOT 0.904325 A little bit needs to improve 0.003165 Agent NAVI 1.000834 Approximately optimal balance FB 1.000834 Approximately optimal balance SBOT 1.270882 A little bit needs to improve BS QBOT 0.903256 A little bit needs to improve 0.005242 Agent WBOT 0.903256 A little bit needs to improve

Finally, the system effectiveness experiment aimed to analyze the actual operation and experience satisfaction of the proposed system APPs. The three aforementioned groups were used as a reference frame for analysis, as shown in Table 10. The satisfaction scale was 1–5 points, representing strongly dissatisfied (1 point) to most satisfied (5 points). It was observed that unrelated departments care more about the interface appearance and the operation of this system being more convenient. The effect was significant in the middle/high age-bracket. Related departments indicated that operation and fluency are somewhat insufficient and should be enhanced. An important indicator was that information correctness was graded as being high. According to this table, fluency is somewhat insufficient, but the graphical menu design of the system APP is easy for the users of the middle/high age-bracket to operate, which is an obvious outcome. Appl. Sci. 2019, 9, 4385 21 of 24

Table 10. System interface design and overall satisfaction analysis.

Related Unrelated Middle/High Topic Item Departments Departments Age-Bracket Interface design 3.5 2.5 3.8 Interface Whether the required 3.9 3.7 3.6 function is found Operation diﬃculty 4.2 3.9 4.2 Operation Operation ﬂuency 3.3 3.1 3.8 If function is perfect 3.2 3.9 4.0 Function If function is practical 4.1 3.8 3.1 If information is correct 4.5 4.3 4.5 Information If information 3.6 3.9 3.7 recommendation is perfect

5. Conclusions and Discussion On the basis of wearable devices, web services, and linked open data, this study’s intelligent travel cloud information monitor and recommendation multi-agent system explored LOD access technologies for OpenData@Taiwan by integrating the aforementioned UAI technology links for increasing the accuracy, authenticity, and integrity of intelligent tourism information, eﬀectively adding value to the quality of cloud information monitoring and recommendation through the support of three-stage intelligent decision-making with CEOonoIAS. This paper extends the application scope of Dr. What-Info and reached the ultimate goal of the research—the construction of an intelligent mobile information monitoring and recommendation multi-agent system with friendly interfaces for autonomously providing corresponding cloud information. The results and conclusions of the system’s practical exploration are listed as follows:

(1) This study established three-tier address-based, UAI-based LOD access technology by actively developing the proposed system with friendly interfaces that assist users in quickly, accurately, and effectively obtaining relevant and timely useful links and open information. (2) With the operational support of CEOntoIAS, this study established a cloud interactive paradigm supported by web services through the corresponding query semantic content with the CURRL in JSON format. With the support of three-stage intelligent decision-making, such as OntoDMA, OntoCBRA, and OntoIAS, the proposed system APPs autonomously and effectively added value to the quality of the monitoring and to the recommendation of intelligent tourism information. Therefore, this study is unique and different from other information agents found in the literature.

Furthermore, regarding the evaluation results mentioned above, the mobile APP, as proposed in this system, can be easily solved, which is completely in line with the original intention of the research—timely, appropriate, and free, as well as providing the latest and complete mobile information services. In addition, based on Tu’s comparison table for LBSs, the proposed system receives an overall function up-to-standard rate of 87.5%, and such recommendations provide users with high information correctness and user satisfaction. The proposed system architecture not only uses the back-end database more efficiently; it also provides users with more all-inclusive LBS. The satisfaction score of the usability of the proposed system and the ease of using the interface design was 84 points in terms of Quesenbery’s 5Es and Nielsen ratings, which indicated that this system APP must be improved in two aspects: error tolerance and help documents. The verification results of the interface design showed that the human–machine interface of our proposed system can meet important design preferences and provide approximate optimal balance with average BI = 0.008430. Finally, although the interface appearance is more convenient, the operation and fluency are somewhat insufficient, and should be enhanced. Information correctness was graded as being high, and the graphical menu design of the system APP was easy for users to use, which were obvious outcomes. Although there Appl. Sci. 2019, 9, 4385 22 of 24 is plenty of room for improvement in experience and in more travel-related agents, such as instant traffic assistance agents and trip planning agents, the feasibility of the proposed service architecture has been proven.

Author Contributions: This research article contains two authors, including K.-Y.C. and S.-Y.Y.; K.-Y.C. and S.-Y.Y. jointly designed the overall architecture and related algorithms and also conceived and designed the experiments; however, Prof. S.-Y.Y. coordinated the overall plan and direction of the experiments and related skills. K.-Y.C. and S.-Y.Y. not only contributed the analysis tools but also analyzed the data. K.-Y.C. performed the experiments and S.-Y.Y. wrote this paper and related replies. Acknowledgments: The authors would like to thank Yu-Jui Tsai, Yong-Han Tsai, and Chi-Ling Ku for their assistance in the earlier system implementation and preliminary experiments. This research was sponsored under grant 106-2221-E-129-008 and 107-2632-E-129-001 by the Ministry of Science and Technology, Taiwan, R.O.C. The authors feel deeply indebted to the Department of Information Management and the Department of Information and Communication at St. John’s University, Taiwan, for all aspects of assistance provided. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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