• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.109-135
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• 2021

his study classified the development process of artificial intelligence (AI) speakers through analysis of the news text of artificial intelligence (AI) speakers shown in traditional news reports, and identified the characteristics of each product by period. The theoretical background used in the analysis are news frames and topic frames. As analysis methods, topic modeling and semantic network analysis using the LDA method were used. The research method was a content analysis method. From 2014 to 2019, 2710 news related to AI speakers were first collected, and secondly, topic frames were analyzed using Nodexl algorithm. The result of this study is that, first, the trend of topic frames by AI speaker provider type was different according to the characteristics of the four operators (communication service provider, online platform, OS provider, and IT device manufacturer). Specifically, online platform operators (Google, Naver, Amazon, Kakao) appeared as a frame that uses AI speakers as'search or input devices'. On the other hand, telecommunications operators (SKT, KT) showed prominent frames for IPTV, which is the parent company's flagship business, and 'auxiliary device' of the telecommunication business. Furthermore, the frame of "personalization of products and voice service" was remarkable for OS operators (MS, Apple), and the frame for IT device manufacturers (Samsung) was "Internet of Things (IoT) Integrated Intelligence System". The econd, result id that the trend of the topic frame by AI speaker development period (by year) showed a tendency to develop around AI technology in the first phase (2014-2016), and in the second phase (2017-2018), the social relationship between AI technology and users It was related to interaction, and in the third phase (2019), there was a trend of shifting from AI technology-centered to user-centered. As a result of QAP analysis, it was found that news frames by business operator and development period in AI speaker development are socially constituted by determinants of media discourse. The implication of this study was that the evolution of AI speakers was found by the characteristics of the parent company and the process of co-evolution due to interactions between users by business operator and development period. The implications of this study are that the results of this study are important indicators for predicting the future prospects of AI speakers and presenting directions accordingly.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.96-105
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• 2018

The V-world, Spatial information open platform map service, provides various national spatial data. Recently, with the development of IT technology, demand for 3D geospatial data that can be merged with new industries such as Internet of Things(IoT) and autonomous vehicles is increasing. Because 3D geospatial data is large and complex, many computer resources are used to provide map services. Most of the 3D map services, such as Vworld, are constructed binary data in consideration of performance. However, this type of data is incompatible because it is difficult to use in other services if there is no precise understanding of the specification. In this paper, we propose a data serialization method to improve the compatibility of new Vworld 3D format which is constructed in binary form. The performance of binary data and serialized binary data is tested and compared. As a result, it is expected that the data using the serialization technique will be similar to the binary data and contribute to improve compatibility.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.43-50
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• 2019

Block-chain enjoys technical advantages such as "robust security," owing to the structural characteristic that forgery is impossible, decentralization through sharing the ledger between participants, and the hyper-connectivity connecting Internet of Things, robots, and Artificial Intelligence. As a result, public organizations have highly positive attitudes toward the adoption of technology using block-chain, and the design of university information services is no exception. Universities are also considering the application of block-chain technology to foundations that implement various information services within a university. Through case studies of block-chain applications across various industries, this study designs an empirical model of an integrated information service platform that integrates information systems in a university. A basic road map of university information services is constructed based on block-chain technology, from planning to the actual service design stage. Furthermore, an actual empirical model of an integrated information service in a university is designed based on block-chain by applying this framework.

• The Journal of Society for e-Business Studies
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• v.21 no.4
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• pp.137-150
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• 2016

A variety of O2O (Online to Offline) services such as Uber, Airbnb, and KakaoTaxi that create new values by connecting offline and online have emerged in the world. O2O services support the convenience of online services and the real-life capability of offline services simultaneously. Thanks to the development of ICTs such as mobility, IoT (Internet of Things), Fin-tech, and big data technologies, the market size of O2O industry is growing very rapidly. This paper reviews various O2O services and industry trends in Korea. In addition, important issues on O2O services and industry promotion are reviewed and presented. Specifically, legislative issues regulating the O2O industry in Korea, interest conflicts between new O2O platform providers and existing traditional offline service providers, current technology infrastructures for O2O services, and negative or side effects of O2O services are reviewed and summarized. Finally, comprehensive policy directions are proposed based on these reviewed issues. It is expected that the proposed policy directions would be adopted by the government and this research could consequently contribute to strengthening the competitiveness of the O2O industry in Korea.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.332-342
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• 2018

This study is aimed at suggesting information about technical choices for designing LED camping lights based on emotional lighting contents of integrated IOT and design areas which take a central role in creation and knowledge based industries and the procedure for materializing them. 'i-Light,' a portable LED camping light, is 'connected lighting' connecting men, space and emotion and a smart camping light based on IOT and emotional lighting contents. 'i-Light' has two functions. One is about lighting for adjusting color and color temperature naturally and the other is about safety for detecting harmful gases. 'i-Light' also has various emotional functions for experiencing interaction and taste of light. For the purpose, portable LED camping lights were designed, first of all, and then a highly color rendering/full-color lighting module, a smart sensor module and an IOT device platform were developed. In addition, efforts were made to establish detailed data about emotional lighting contents and to develop a Web application based on them. Finally, prototypes of portable LED camping lights were made to get a test bench and usability evaluation from related organizations. According to the results, all of 12 developed emotional lighting contents and three IOT safety sensors were suitable and prototypes were satisfactory. This paper will suggest a direction about actual technical choices for development of contents and products integrating artificial intelligence and big data and about the procedure for materializing them.