• Knowledge Management Research
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• v.22 no.2
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• pp.145-165
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• 2021

As of late December 2019, the spread of COVID-19 pandemic began which put the entire world in panic. In order to overcome the crisis and minimize any subsequent damage, the government as well as its affiliated institutions must maximize effects of pre-existing policy support and introduce a holistic response plan that can reflect this changing situation- which is why it is crucial to analyze social topics and people's interests. This study investigates people's major thoughts, attitudes and topics surrounding COVID-19 pandemic through the use of social media and big data. In order to collect public opinion, this study segmented time period according to government countermeasures. All data were collected through NAVER blog from 31 December 2019 to 12 December 2020. This research applied TF-IDF keyword extraction and LDA topic modeling as text-mining techniques. As a result, eight major issues related to COVID-19 have been derived, and based on these keywords, this research presented policy strategies. The significance of this study is that it provides a baseline data for Korean government authorities in providing appropriate countermeasures that can satisfy needs of people in the midst of COVID-19 pandemic.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.5
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• pp.90-102
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• 2022

In recent years, as interest in healing increases, outdoor spaces with the concept of healing have been created. For more professional and in-depth planning and design, the perception and characteristics of outdoor healing places through social media posts were analyzed using NER. Text mining was conducted using 88,155 blog posts, and frequency analysis and clique cohesion analysis were conducted. Six elements were derived through a literature review, and two elements were added to analyze the perception and the characteristics of healing places. As a result, visitors considered place elements, date and time, social elements, and activity elements more important than personnel, psychological elements, plants and color, and form and shape when visiting healing places. The analysis allowed the derivation of perceptions and characteristics of healing places through keywords. From the results of the Clique, keywords, such as places, date and time, and relationship, were clustered, so it was possible to know where, when, what time, and with whom people were visiting places for healing. Through the study, the perception and characteristics of healing places were derived by analyzing large-scale data written by visitors. It was confirmed that specific elements could be used in planning and marketing.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.5
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• pp.61-69
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• 2015

We are in overload status of information not just in a flood of information due to the data pouring from various kinds of mobile devices, online and Social Network Service(SNS) every day. While there are many existing information already created, lots of new information has been created from moment to moment. Linkage analysis has the shortcoming in that it is difficult to find the information we want since the number of rules increases geometrically as the number of item increases with the method of finding out frequent item set where the frequency of item is bigger than minimum support in this information. In this regard, this thesis proposes WT-algorithm that represents the transaction data set as Boolean variable item and grants weight to each item by making algorithm with Quine-McKluskey used to simplify the logical function. The proposed algorithm can improve efficiency of data mining by reducing the unnecessary rules due to the advantage of simplification regardless of number of items.

• The Journal of the Korea Contents Association
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• v.16 no.6
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• pp.671-679
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• 2016

We investigated subgroups within a global communication network to improve the empirical understanding of global communication phenomenon from the social network perspective. We collected global communication data from Twitter and constructed a global communication network. We also added countries' geographic and economic properties used in the United Nations and the World Economic Forum. We analyzed the subgroups' structure within the global communication network using centrality analysis, core-peripheral analysis, and cohesion analysis. We also detected communities embedded within the global communication network with modularity-based community detection methods. We found that the core countries occupy central positions in the global communication network and there is a hierarchical communication structure among the economic subgroups. Futhermore, we discovered some communities within the global communication network and found that countries within the communities can have homophily such as economy, geography, history, culture, and religion.

• Journal of KIISE
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• v.45 no.1
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• pp.53-60
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• 2018

This study describes a spatial web object retrieval system using Spark, an in - memory based distributed processing system. Development of social networks has created massive amounts of spatial web objects, and retrieval and analysis of data is difficult by using exist spatial web object retrieval systems. Recently, development of distributed processing systems supports the ability to analyze and retrieve large amounts of data quickly. Therefore, a method is promoted to search a large-capacity spatial web object by using the distributed processing system. Data is processed in block units, and one of these blocks is converted to RDD and processed in Spark. Regarding the discussed method, we propose a system in which each RDD consists of spatial web object index for the included data, dividing the entire spatial region into non-overlapping spatial regions, and allocating one divided region to one RDD. We propose a system that can efficiently use the distributed processing system by dividing space and increasing efficiency of searching the divided space. Additionally by comparing QP-tree with R-tree, we confirm that the proposed system is better for searching the spatial web objects; QP-tree builds index with both spatial and words information while R-tree build index only with spatial information.

• 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.

• Information Systems Review
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• v.25 no.4
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• pp.233-248
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• 2023

The increasing integration of intelligent information technologies within organizational systems has amplified the risk to personal information security. This escalation, in turn, has fueled growing apprehension about an organization's capabilities in safeguarding user data. While Internet users adopt a multifaceted approach in assessing a company's information security, existing research on the multiple dimensions of information security is decidedly sparse. Moreover, there is a conspicuous gap in investigations exploring whether users' evaluations of organizational information security differ across industry types. With an aim to bridge these gaps, our study strives to identify which information security attributes users perceive as most critical and to delve deeper into potential variations in these attributes across different industry sectors. To this end, we conducted a structured survey involving 498 users and utilized the analytic hierarchy process (AHP) to determine the relative significance of various information security attributes. Our results indicate that users place the greatest importance on the technological dimension of information security, followed closely by transparency. In the technological arena, banks and domestic portal providers earned high ratings, while for transparency, banks and governmental agencies stood out. Contrarily, social media providers received the lowest evaluations in both domains. By introducing a multidimensional model of information security attributes and highlighting the relative importance of each in the realm of information security research, this study provides a significant theoretical contribution. Moreover, the practical implications are noteworthy: our findings serve as a foundational resource for Internet service companies to discern the security attributes that demand their attention, thereby facilitating an enhancement of their information security measures.

• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.768-773
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• 2015

Emergence events are the cause of much economic damage. In order to minimize the damage that these events cause, it must be possible to predict what will happen in the future. Accordingly, many researchers have focused on real-time monitoring, detecting events, and investigating events. In addition, there have also been many studies on predictive analysis for forecasting of future trends. However, most studies provide future tendency per event without contextual compositive analysis. In this paper, we present a predictive visual analytics system using topic composition to provide future trends per event. We first extract abnormal topics from social media data to find interesting and unexpected events. We then search for similar emergence patterns in the past. Relevant topics in the past are provided by news media data. Finally, the user combines the relevant topics and a new context is created for contextual prediction. In a case study, we demonstrate our visual analytics system with two different cases and validate our system with possible predictive story lines.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.292-297
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• 2014

As a Social Network Service (SNS) has become an integral part of our everyday lives, millions of users can express their opinion and share information regardless of time and place. Hence sentiment analysis using micro-blogs has been studied in various field to know people's opinion on particular topics. Most of previous researches on movie reviews consider only positive and negative sentiment and use it to predict movie rating. As people feel not only positive and negative but also various emotion, the sentiment that people feel while watching a movie need to be classified in more detail to extract more information than personal preference. We measure sentiment distributions of each movie from tweets according to the Thayer's model. Then, we find similar movies by calculating similarity between each sentiment distributions. Through the experiments, we verify that our method using micro-blogs performs better than using only genre information of movies.

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.17-24
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• 2018

This study attempted to analyze technology convergence structure and key technology research sectors in bio-health. For this, network analysis was performed based on the patent outcomes achieved through national R&Ds. Then, a patent network was analyzed to derive problems and collect data from the National Science & Technology Information Service. With the five groups obtained through the analysis of IPC network and national R&D patents in bio-health based on a research frame network, topics were chosen based on the bio-healthcare technology system. Then, the technology with the greatest ripple effects was derived and compared to other sectors, suggesting a direction for national R&D investments. It is anticipated that this study would make a contribution to a search for R&D investment direction by additionally analyzing overseas patent data and improving correlation analysis between technology convergence and government-led R&D expenses.