• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.1
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• pp.47-57
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• 2015

Recently, Internet of Things (IoT) have become popular for research and development in many areas. IoT makes a new intelligent network between things, between things and persons, and between persons themselves. Social network service technology is in its infancy, but, it has many benefits. Adjacent users in a social network tend to trust each other more than random pairs of users in the network. In this paper, we propose recommendation technique using social network in Internet of Things environment. We study previous researches about information recommendation, IoT, and social IoT. We proposed SIoT_P(Social IoT Prediction) using social relationships and item-based collaborative filtering. Also, we proposed SR(Social Relationship) using four social relationships (Ownership Object Relationship, Co-Location Object Relationship, Social Object Relationship, Parental Object Relationship). We describe a recommendation scenario using our proposed method.

• Journal of Digital Contents Society
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• v.12 no.3
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• pp.361-370
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• 2011

Recent popularity of the smartphone market, with the explosive growth of mobile location-based SNS(Social Network Service), is into the mainstream. SNS aims to cooperate and share information with all users, but mobile SNS is still not easy people with disabilities. In addition to using the Web, it has difficulty to provide an appropriate interface to access such as smartphone for people with disabilities. This paper will specify and analyze the context-awareness technology and SNS function, and study a variety of information that students with disabilities such as time, location and activity states including data collection and analysis of the situation by converting the status information. It has provide that students with disabilities to lead comfortable life in school according to the various types of disabilities, and school information and safety services using mobile location-based SNS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.1909-1928
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• 2020

Predicting human mobility has always been an important task in Location-based Social Network. Previous efforts fail to capture spatial dependence effectively, mainly reflected in weakening the location topology information. In this paper, we propose a neural network-based method which can capture spatial-temporal dependence to predict the next location of a person. Specifically, we involve a graph convolutional network (GCN) based on a seq2seq framework to capture the location topology information and temporal dependence, respectively. The encoder of the seq2seq framework first generates the hidden state and cell state of the historical trajectories. The GCN is then used to generate graph embeddings of the location topology graph. Finally, we predict future trajectories by aggregated temporal dependence and graph embeddings in the decoder. For evaluation, we leverage two real-world datasets, Foursquare and Gowalla. The experimental results demonstrate that our model has a better performance than the compared models.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.3
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• pp.27-35
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• 2011

Social network services are developed which is based on interaction and collaboration between users. This used to teaching-learning and integrate personal experience based on constructivism and social learning has developed into. In order to use which better to support the N-Screen communication model is needed. Communication model is to support the interaction between learner-instructor- the system. However, until now, There are a lot of web-based communications research. In this study, Social Learning Services environment to extended to N-Screen. For seamless service, Location information of individuals to use to learning activities. To support this, the communication manager is to design and implement. Communications manager for the N-Screen services draw students use cases and define the required functions. Based on this, Communication function is designed. In addition, Considering the characteristics of each device, personal location information to be reflected.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.428-435
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• 2016

Hundreds of millions of new posts and information are being uploaded and propagated everyday on Online Social Networks(OSN) like Twitter, Facebook, or Instagram. This paper proposes and implements a GPS-location based SNS data mapping, analysis, and visualization system, called Smart SNS Map, which collects SNS data from Twitter and Instagram using hundreds of PlanetLab nodes distributed across the globe. Like no other previous systems, our system uniquely supports a variety of functions, including GPS-location based mapping of collected tweets and Instagram photos, keyword-based tweet or photo searching, real-time heat-map visualization of tweets and instagram photos, sentiment analysis, word cloud visualization, etc. Overall, a system like this, admittedly still in a prototype phase though, is expected to serve a role as a sort of social weather station sooner or later, which will help people understand what are happening around the SNS users, systems, society, and how they feel about them, as well as how they change over time and/or space.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.60-63
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• 2011

인터넷은 단순한 정보습득을 위한 공간을 넘어 사람들이 모여 교류하는 '소통의 장'으로서 역할을 하고 있다. 최근에는 타인과 끊임없이 대화를 나누고 소통하려는 인간의 기본 욕구를 반영한 서비스인 SNS(Social Network Service)가 전 세계의 주목을 받고 있다. 또한 LBS(Location-Based Service)는 GPS, Wi-Fi를 통한 위치정보를 활용하여 업무생산성 개선 및 다양한 생활 편의를 제공하는 서비스이다. 본 논문에서는 안드로이드 OS를 기반으로 LBS와 SNS를 통합한 어플리케이션을 제안한다. 개인용 모바일 정보기기인 스마트폰을 활용하여, 위치정보에 이용자 정보, SNS를 결합하여 서비스를 고도화 한다. 지인들의 위치를 기반으로 현재 상태, 트윗(Twit)을 통해 정보의 공유 및 활용을 극대화 할 수 있다. 이를 통해 사용자들이 적극적이고 용이하게 온라인 Identity를 표현하는 것을 목적으로 본 시스템을 제안한다.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.277-287
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• 2014

In LBSNS(Location-based Social Network Service), users can share locations and communicate with others by using check-in data. The check-in data consists of POI name, category, coordinate and address of locations, nickname of users, evaluating grade of locations, related article/photo/video, and etc. If you analyze the check-in data from the location-based social network service in accordance with your situation, you can provide various customized services. Therefore, In this paper, we develop a location recommendation system based on LBSNS that can utilize the check-in data efficiently. This system analyzes the location category of the check-in data, determines the weighted value of it, and finds out the similarity between users by using the Pearson correlation coefficient. Also, it obtains the preference score of recommended locations by using the collaborated filtering algorithm and then, finds out the distance score by applying the Euclidean's algorithm to the recommended locations and the current users' locations. Finally, it recommends appropriate locations by applying the weighted value to the preference score and the distance score. In addition, this paper approved excellence of the proposed system throughout the experiment using real data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.802-804
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• 2017

Nowadays the development of mobile systems occupies significant part of our life, and users using mobile phones are demanding the utilization of differentiated spatial information. A study on application which provides information according to individual location information such as mobile advertisement is conducted for the location-based mobile service. In order to allow mobile phone users to share information based on local information, this paper proposes a mobile sharing system which combines social networks and location-based service to provide efficient information based on location information in mobile phones. The application can be provided with information associated with its location when the mobile phone user arrives at a specific location. Another proposed method focuses on providing more appropriate services for individual situations compared to other applications, and can contribute to expanding social networking functions to facilitate information sharing among users.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.3
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• pp.1-8
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• 2010

Recently, the missing due to the heinous crimes occurred in succession has been the focus of attention, so measures for these are urgently needed. So, it is necessary that we need to check and take care the weak like children, women, the elderly with dementia and insane person to prevent these social problems. This study is location-based service that suggests the tracking system and way to guard a family and property from the various risk factors which are possible in our life. To acquire the location of the user who is in motion, a communications network technologies were integrated and the system will be able to be monitoring in real time through the website. So, it is convenient and safe because it is possible to use anywhere. Concretely, it was described around location-based service, GPS system, tracking system, process of the service solution and composition for the system. Also, the way to activate the system will be suggested. The final suggested system was verified the possibility through the placement. With this, i will offer effective measures which is more convenient to use and reliable.

• The Journal of the Korea Contents Association
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• v.17 no.8
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• pp.619-630
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• 2017

With the growing popularity of smart devices, various location based services have been providing to users. Recently, some location based social applications that combine social services and location based services have been emerged. The demands of a k-nearest neighbors(k-NN) query which finds k closest locations from a user location are increased in the location based social network services. In this paper, we propose an approximate k-NN query processing method for fast response time in a large number of users environments. The proposed method performs efficient stream processing using big data distributed processing technologies. In this paper, we also propose a modified grid index method for indexing a large amount of location data. The proposed query processing method first retrieves the related cells by considering a user movement. By doing so, it can make an approximate k results set. In order to show the superiority of the proposed method, we conduct various performance evaluations with the existing method.