• Informatization Policy
• /
• v.27 no.2
• /
• pp.66-83
• /
• 2020

Local issues that occur in cities typically garner great attention from the public. While local governments strive to resolve these issues, it is often difficult to effectively eliminate them all, which leads to complaints. In tackling these issues, it is imperative for local governments to use big data to identify the nature of complaints, and proactively provide solutions. This study applies the LDA topic modeling technique to research and analyze trends and patterns in complaints filed online. To this end, 9,625 cases of online complaints submitted to the city of Busan from 2015 to 2017 were analyzed, and 20 topics were identified. From these topics, key topics were singled out, and through analysis of quarterly weighting trends, four "hot" topics(Bus stops, Taxi drivers, Praises, and Administrative handling) and four "cold" topics(CCTV installation, Bus routes, Park facilities including parking, and Festivities issues) were highlighted. The study conducted big data analysis for the identification of trends and patterns in civil affairs and makes an academic impact by encouraging follow-up research. Moreover, the text mining technique used for complaint analysis can be used for other projects requiring big data processing.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.8
• /
• pp.535-544
• /
• 2018

As the spread of mobile devices equipped with various sensors and high-quality wireless network communications functionsexpands, the amount of spatio-temporal data generated from mobile devices in various service fields is rapidly increasing. In conventional research into processing a large amount of real-time spatio-temporal streams, it is very difficult to apply a Hadoop-based spatial big data system, designed to be a batch processing platform, to a real-time service for spatio-temporal data streams. This paper extends the MapReduce online framework to support real-time query processing for continuous-input, spatio-temporal data streams, and proposes a load management method to distribute overloads for efficient query processing. The proposed scheme shows a dynamic load balancing method for the nodes based on the inflow rate and the load factor of the input data based on the space partition. Experiments show that it is possible to support efficient query processing by distributing the spatial data stream in the corresponding area to the shared resources when load management in a specific area is required.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.8
• /
• pp.2751-2771
• /
• 2022

In the post covid-19 era, organizations will experience a new environment. Advances in technologies such as AI and big data, and new experiences such as online meetings and lectures, will increase the use of online communication. Businesses will increasingly engage in online-based information sharing, virtual team operations, and online meetings. This study focuses on meeting climate and satisfaction, to improve the performance of online meetings. Existing studies on meeting climate presuppose off-line situations. Offline and online communication methods and meeting formats are different. This paper proposes new climate types to develop an appropriate climate for online-based meetings. To apply these climates in online meetings, a measurement scale was developed and the impact on online meeting satisfaction was verified. As a result of the study, it was found that the creativity-oriented meeting climate was the most important, and relation-oriented and participation-oriented meeting climates also had a significant effect, while the direction-oriented and task-oriented climates were relatively less important. This study develops new variables and measurements for online meeting climates, and explains their importance. Companies will be able to leverage the appropriate climates for online meetings to improve performance.

• Journal of Distribution Science
• /
• v.17 no.5
• /
• pp.85-94
• /
• 2019

Purpose - The purpose of this study is to analyze the impact of customer's communication on sales performance in the online market. Research design, data, and methodology - This study uses linear regression analysis to examine the effects of product review characteristics which are the result of customer's communication, on sales performance by using product reviews of online marketplace Amazon. Result - The increase in the number of product reviews positively affected sales performance. An increase in extreme opinions in the product review has a positive effect on sales performance. The product review length has a negative effect on sales performance. Conclusions - This study has shown the online marketplace customers' communication can influence sales performance using product review big data. This study contributed to the theoretical completeness by analyzing all the products of the book category in Amazon online market. This research will complement the theories regard to the customer behavior affecting sales performance. We expect the empirical analysis result will provide empirical help to sellers, online marketplace operators, and customers. In particular, the number of letters in the product may negatively affect sales performance, so sellers need to consider this effect carefully when exposing product reviews.

• Journal of Distribution Science
• /
• v.22 no.6
• /
• pp.115-122
• /
• 2024

Purpose: The main aim of this study is to analyze and suggest new online music distribution models targeted to facilitate the development of the Korean Wave (Hallyu) music market in all locations of the world. This study is conducted through a close analysis of the prevailing distribution models, the unique challenges of the K-pop market, and the trends in new technologies. Research design, data and methodology: To address the issue of how the online music distribution market could be domesticated for the Korean music industry, a systematic review of the previous studies was conducted. The use of the PRISMA approach was followed so that an accurate and transparent method for choosing the studies is ensured. Results: According to the investigation of literature analysis, the online distribution strategy may consist of four key plannings as follows, 1. Leveraging Social Media and User-Generated Content Platforms, 2. Embracing Immersive and Interactive Experiences, 3. Fostering Direct-to-Fan Connections and Monetization, 4. Harnessing Artificial Intelligence and Big Data Analytics. Conclusions: Finally, collaboration and strategic partnerships will be vital. The Korean music companies should seek to cooperate with the technology companies, social media platforms, and the global music streaming services so that they can grow their market, acquire new technologies, and to better their online distribution strategies.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.7
• /
• pp.1083-1091
• /
• 2017

As big data technologies have been developed and massive data have exploded from users through various channels, CEO of global IT enterprise mentioned core importance of data in next generation business. Therefore various machine learning technologies have been necessary to apply data driven services but especially recommendation has been core technique in viewpoint of directly providing summarized information or exact choice of items to users in information flooding environment. Recently evolved recommendation techniques have been proposed by many researchers and most of service companies with big data tried to apply refined recommendation method on their online business. For example, Amazon used item to item collaborative filtering method on its sales distribution platform. In this paper, we develop a commercial web service for suggesting music contents and implement three representative collaborative filtering methods on the service. We also produce recommendation lists with three methods based on real world sample data and evaluate the usefulness of them by comparison among the produced result. This study is meaningful in terms of suggesting the right direction and practicality when companies and developers want to develop web services by applying big data based recommendation techniques in practical environment.

• The Journal of the Korea Contents Association
• /
• v.21 no.8
• /
• pp.546-558
• /
• 2021

This study focused on analyzing the contents of social big data produced in the online space, dealing with issues related to food in the community in the context of COVID-19. First, this study analyzed food-related issues that spread through regional websites and online community(cafes) after social distancing was implemented due to COVID-19. Next, this study analyzed the contents of food-related issues that spread through media news, SNS, and portals. As a result, there were more food-related posts on the homepages of other regions compared to the metropolitan areas such as Seoul and Gyeonggi, but in the case of online communities, there were more food-related issues in online communities registered in Seoul and Gyeonggi regions. Food-related keywords in regional online communities mainly contained content related to the local economy. In the media articles, SNS, and search portal issues, content that can be discussed in the consumption process of local community food-related policies, information, and products mainly appeared. Based on the results of the study, it was found that there is no specialized information sharing system for each community, that online communities can contribute to providing food information applicable to reality, and that it is possible to verify the performance of regional food policies through social media.

• International journal of advanced smart convergence
• /
• v.13 no.1
• /
• pp.108-113
• /
• 2024

The purpose of this study is to analyze user perceptions of tourism platforms through big data. Data were collected from Naver, Daum, and Google as big data analysis channels. Using semantic network analysis with the keyword 'tourism platform,' a total of 29,265 words were collected. The collection period was set for two years, from August 31, 2021, to August 31, 2023. Keywords were analyzed for connected networks using TexTom and Ucinet programs for social network analysis. Keywords perceived by tourism platform users include 'travel,' 'diverse,' 'online,' 'service,' 'tourists,' 'reservation,' 'provision,' and 'region.' CONCOR analysis revealed four groups: 'platform information,' 'tourism information and products,' 'activation strategies for tourism platforms,' and 'tourism destination market.' This study aims to expand and activate services that meet the needs and preferences of users in the tourism field, as well as platforms tailored to the changing market, based on user perception, current status, and trend data on tourism platforms.

• The Journal of the Korea Contents Association
• /
• v.18 no.10
• /
• pp.229-235
• /
• 2018

The purpose of this study is to propose a method to utilize Big Data Analysis to find policy issues of local governments in the reality that utilization of big data becomes increasingly important in efficient and effective policy making process. For this purpose, this study analyzed the 180,000 articles of Suwon city for the past three years and identified policy issues and evaluated policy priorities through IPA analysis. The results of this study showed that the analysis of semi-formal big data through newspaper articles is effective in deriving the differentiated policy issues of different local autonomous bodies from the main issues in the nation, In this way, the methodology of finding policy issues through the analysis of big data suggested in this study means that local governments can effectively identify policy issues and effectively identify the people. In addition, the methodology proposed in this study is expected to be applicable to the policy issues through the analysis of various semi - formal and informal big data such as online civil complaint data of the local government, resident SNS.

• The Journal of the Korea Contents Association
• /
• v.18 no.5
• /
• pp.367-375
• /
• 2018

The study aims to determine which groups leave longer(more active) online reviews(comments) on the film by separating groups, one that satisfied with the movie while the other group dissatisfied with the movie. The data used were rating scores and reviews(comments) from Naver Movie API, and break-even point data provided by Korea Film Commission. We analyzed the relationship between movie rating and review length, before and after movie opening, the characteristics of review length according to the box office, and whether the movie rating affects the review length.