• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.623-625
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• 2019

인공지능과 소프트웨어가 중요한 역할을 하는 시대가 되었고, 이를 학생들에게 교육하여 미래의 AI/SW 인재를 양성하는 것에 많은 관심이 집중되고 있다. 해외 주요국에서는 이러한 시대적 흐름에 맞추어 AI/SW 분야의 인재 양성을 위해 노력하고 있으며, 국내에서도 여러 부처에서 관련된 다양한 정책을 시행하고 있다. 본 논문에서는 SW교육 관련 소셜미디어와 언론 데이터를 수집하고 이를 분석하여 국내 AI/SW교육에 대한 시사점을 제시하려고 한다. 이를 위해 2014년부터 2018년까지 총 5개년도의 데이터를 수집하고, 네트워크 분석 방법을 활용하여 연도별 SW교육의 흐름, 주요 등장 키워드, 연관 검색어들을 파악하였다. 이를 활용하여 미래의 AI/SW 교육 정책 수립 및 개선을 위한 시사점을 모색해보고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1710-1712
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• 2012

최근 Twitter를 비롯한 소셜 네트워크 서비스의 급속한 확산으로 인해, 많은 수의 SNS 메시지가 실시간으로 생성되고 있다. 이러한 SNS상에서의 단문 글들을 실시간으로 분석하여 최신의 트렌드를 추출해 낼 수 있다면, 사용자에게 유용한 정보를 제공하는 것이 가능하다. 본 논문에서는 다량의 Tweet글들에 대한 실시간 분석을 바탕으로 트렌드를 추출하고 연관된 키워드를 제공하는 기법을 제안한다. 제안하는 기법은 실시간으로 생성되는 Tweet내에서 영어의 언어적 특성을 활용하여 최근 이슈화된 트렌드 키워드를 추출해낸다. 또한, Tweet 내에서 각 트렌드 키워드간 관계를 분석하여 연관 키워드를 제공하며, 동시에 Wikipedia와 Google에서의 검색을 통하여 다른 형태의 연관 키워드도 추출한다. 이 모든 과정은 제안된 트렌드 추출 알고리즘을 통해 실시간으로 제공된다. 제안된 기법을 바탕으로 시스템을 구현하고 다양한 실험을 통하여 키워드의 유효성 및 처리 속도 면에서 시스템의 성능을 평가한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.376-377
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• 2013

스마트폰이 도입된지 불과 몇 년만에 모바일 컴퓨팅은 이제 생활과 밀접한 다양한 콘텐츠를 소비하고 공유할 수 있는 공간이자 도구가 되었다. 이제 우리의 일상은 스마트 폰의 어플리케이션이나 SNS 등을 이용해 보다 합리적인 정보 탐색을 위하여 얻고자 하는 정보를 검색하고 실시간으로 공유한다. 최근 선호하는 스마트폰 어플리케이션은 오락, 유틸리티, 소셜 네트워크의 비중이 크다. 본 논문에서는 이러한 스마트 폰의 흐름에 발 맞춰, Android기반 게임 애플리케이션을 구현하고자 한다. 본 애플리케이션은 컴퓨터에서만 즐길 수 있던 게임에서 벗어나 보다 접근성이 편리한 스마트폰에서 실행할 수 있을 뿐 아니라 커뮤니케이션 인프라(별점 주기, 랭킹)와 item shop 기능을 제공한다. 본 연구에서는 더블버퍼링, 각종 센서, Surface View 등을 활용하여 스마트폰 게임 애플리케이션을 제작한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.279-280
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• 2020

최근 소셜 미디어로 이름을 알린 이색 카페와 맛집을 찾아다니는 문화가 확산되는 추세이다. 블로그 포털 검색을 통해 찾아본 맛집은 광고성 게시물이 많아서 신뢰도가 떨어지고, 맛집 관련 게시물 수가 많아서 모든 게시물들을 수동으로 읽기는 불가능하다. 본 논문에서는 사용자들이 선호해서 자발적으로 공유하는 신뢰도 높은 인스타그램의 맛집 포스트 데이터를 이용하여 아이템 기반의 협업 필터링(Item-based Collaborative Filtering) 기법을 통해 사용자의 취향에 맞고 선호할 만한 맛집을 자동으로 추천해주는 알고리즘 및 시스템을 소개한다.

• Journal of Korean Society of Archives and Records Management
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• v.18 no.3
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• pp.165-191
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• 2018

The National Archives of Korea and other archives now provide services through social media such as Facebook or Twitter, and use such platforms to interact with users. To be specific, users communicate with and get information from these archives via the National Sinmoongo, the place for civil petition. Therefore, it is significant to understand the users' needs and the contents of the communication by analyzing the comments and petitions that have appeared in these channels. For this, this study analyzed users' perceptions and information needs shared through social media and the National Sinmoongo of the National Archives of Korea. The social media content analyzed here were posts and comments from the Facebook accounts of the National Archives of Korea, e-Record, the Busan Archives, and the Presidential Archives of Korea. Also, sentences containing the words "National Archives of Korea" and "Presidential Archives of Korea" that have appeared in texts in the National Sinmoongo were analyzed. Based on the analysis results, suggestions that could activate the user-centered archival information services in the archives and records centers were made.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.4
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• pp.171-180
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• 2016

As social networking services such as twitter become increasingly popular, data streams are widely prevalent these days. In order to search data accumulated from data streams efficiently, the use of an index structure is essential. In this paper, we propose an update-efficient, disk-based inverted index structure for efficient keyword search on data streams. When new data arrive at the data stream, the index needs to be updated to incorporate the new data. The traditional inverted index is very inefficient to update in terms of disk I/O, because all index data stored in the disk need to be read and written to the disk each time the index is updated. To solve this problem, we divide the whole inverted index into a sequence of inverted indices with exponentially increasing size. When new data arrives, it is first inserted into the smallest index and, later, the small indices are merged with the larger indices, which leads to a small amortize update cost for each new data. Furthermore, when indices stored in the disk are merged with each other, we minimize the disk I/O cost incurred for the merge operation, resulting in an even smaller update cost. Through various experiments, we compare the update efficiency of the proposed index structure with the previous one, and show the performance advantage of the proposed structure in terms of the update cost.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.1
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• pp.76-81
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• 2018

The Internet of Things (IoT), which is emerging as a future economic growth engine, has been actively introduced in areas close to our daily lives. However, there are still IoT security threats that need to be resolved. In particular, with the spread of smart homes and smart cities, an explosive amount of closed-circuit televisions (CCTVs) have been installed. The Internet protocol (IP) information and even port numbers assigned to CCTVs are open to the public via search engines of web portals or on social media platforms, such as Facebook and Twitter; even with simple tools these pieces of information can be easily hacked. For this reason, a big-data analytics system is needed, capable of supporting quick responses against data, that can potentially contain risk factors to security or illegal websites that may cause social problems, by assisting in analyzing data collected by search engines and social media platforms, frequently utilized by Internet users, as well as data on illegal websites.

• International Commerce and Information Review
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• v.15 no.2
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• pp.387-410
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• 2013

The competition aspect of business has been transformed from competition among companies to competition among ecosystem, and has been grown to platform based business, which is defined as ecosystem among business. Coming to the spotlight with the advantages of platform business combined software and hardware like Apple, platform business have been emerging in many fields. In this research, we define platform and platform based business, and then review related researches. After this, we review four representative research methodologies which are Yin(2011)' s case analysis research, Eisenhardt(2007)' s case analysis research, Romano etc' s web based qualitative data analysis method(2003), and Creswll(2010)' s open coding technique. And then, we suggest this research' s natural methodology combined with the advantages of four research methodologies. Based on our research methodology, we choose three high commercialized categories, which are smartphone platform business, social platform business, and search engine platform business. And then, we choose seven companies in three categories with success cases & failure cases, and analysis each case in perspective of our research methodology. And then, we suggest critical success & failure elements. Based on our findings, we suggest three strategic elements for the longevity of platform based business. Finally, we suggest the limitations of our research and further research issues.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.5
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• pp.61-76
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• 2018

Everything is coming into the world of network where modern people are living. The Internet of Things that attach sensors to objects allows real-time data transfer to and from the network. Mobile devices, essential for modern humans, play an important role in keeping all traces of everyday life in real time. Through the social network services, information acquisition activities and communication activities are left in a huge network in real time. From the business point of view, customer needs analysis begins with SNS data. In this research, we want to build an automatic collection system of SNS contents of web environment in real time using Python. We want to help customers' needs analysis through the typical data collection system of Instagram, Twitter, and YouTube, which has a large number of users worldwide. It is stored in database through the exploitation process and NLP process by using the virtual web browser in the Python web server environment. According to the results of this study, we want to conduct service through the site, the desired data is automatically collected by the search function and the netizen's response can be confirmed in real time. Through time series data analysis. Also, since the search was performed within 5 seconds of the execution result, the advantage of the proposed algorithm is confirmed.

• Korean journal of communication and information
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• v.66
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• pp.59-86
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• 2014

This study explored how rumor is diffused via Twitter and how the characteristics of rumor affect the interactivity among users in the Korea-U.S. FTA case. A key word search located three issues as major ones related to the Korea-U.S. FTA: appendectomy myth, collapse of health insurance, and increases in medicine prices. The arousal of rumor has two dimensions: fact and expression. The fact arousal was the highest in the issue of 'appendectomy myth', and the expression arousal the highest in 'increases in medicine prices'. The rumor diffusion took the 'explosive wave' in the issue of appendectomy myth, the 'latent wave' in the issue of increase in medicine prices, and the 'repetitive wave' in the issue of collapse of health insurance. Correlation analyses revealed a high correlation between the arousal intensity of rumor and the user interactivity in the issue of collapse of health insurance. The study showed that Twitter took a role of diffusing negative messages about the Korea-U.S. FTA. Results implies that government officials and journalists pay attention to Twitter for sensing the public opinion when building policies and managing crises.