• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.11-17
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• 2022

Currently, the income and funds of the local economy are flowing out to the metropolitan area, and talented people, the driving force for regional development, also gather in the metropolitan area, and the local economy is facing a serious crisis. Local currency is issued by local governments and is a currency with auxiliary and complementary functions that can be used only within the area concerned. In order to revitalize the local economy, as local governments have focused their attention on the introduction of local currency, studies on the issuance and use of local currency are continuously being conducted. In this study, by using big data from data materials such as portals and SNS, the consumer image of local currency issued in local governments was identified through big data analysis, and based on the research results, the issuance and operation of local currency was conducted. The purpose is to present implications for The results of this study are as follows. First, by inducing local consumption through the policy issuance of local currency, it is showing the effect of increasing the economic income of the region. Second, local governments are exerting efforts to revitalize the economy and establish a virtuous cycle system for the local economy by issuing and distributing local currency. Third, the introduction of blockchain technology shows the stable operation of local currency. With academic significance, it was possible to grasp the changed appearance and effect of local currency through big data analysis and the policy direction of local currency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2211-2232
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• 2018

The unstructured and semi-structured big data in social network poses new challenges in query retrieval. This requirement needs to be met by introducing quality retrieval time measures like indexing. Due to the huge volume of data storage, there originate the need for efficient index algorithms to promote query processing. However, conventional algorithms fail to index the huge amount of frequently obtained information in real time and fall short of providing scalable indexing service. In this paper, a new LIndex algorithm, which is a heuristic on Lucene is built on Neo4jHA architecture that holds the social network Big data. LIndex is a flexible and simplified adaptive indexing scheme that ascendancy decomposed shortest paths around term neighbors as basic indexing unit. This newfangled index proves to be effectual in query space pruning of graph database Neo4j, scalable in index construction and deployment. A graph query is processed and optimized beyond the traditional Lucene in a time-based manner to a more efficient path method in LIndex. This advanced algorithm significantly reduces query fetch without compromising the quality of results in time. The experiments are conducted to confirm the efficiency of the proposed query retrieval in Neo4j graph NoSQL database.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.391-402
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• 2021

This study aims to analyze the impact of the company's financial performance in mediating the relationship between Intellectual Capital and GCG on Corporate Value in banking companies listed on the Indonesia Stock Exchange (IDX). Also, this study analyzes the direct effect of intellectual capital and GCG on corporate value and the indirect effect through the company's financial performance. This study develops research of Chen et al. (2005) and measures Intellectual Capital with VAIC (Pulic, 1998). VAIC model is more accurate to measure Intellectual Capital because it can show potential intellectual use efficiently. The data used are banking companies listed on the IDX in 2014-2016 with purposive sampling technique and Data Analysis Technique used are path analysis. The results showed that the financial performance of banking companies was proven to mediate the relationship between intellectual capital and GCG. The role of GCG that can improve financial performance and corporate value is only GCG as measured by the ratio of independent commissioners and audit quality. Meanwhile, the financial performance and corporate value audited by the Big 4 will be greater than the financial performance and corporate value of the banking companies listed on the Indonesia Stock Exchange that are not audited by the Big 4.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.4
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• pp.153-161
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• 2021

Real-time access is required to handle continuous and unstructured data and should be flexible in management under dynamic state. Platform can be built to allow data collection, storage, and processing from local-server or multi-server. Although the former centralize method is easy to control, it creates an overload problem because it proceeds all the processing in one unit, and the latter distributed method performs parallel processing, so it is fast to respond and can easily scale system capacity, but the design is complex. This paper provides data collection and processing on one platform to derive significant insights from various data held by an enterprise or agency in the latter manner, which is intuitively available on dashboards and utilizes Spark to improve distributed processing performance. All service utilize dockers to distribute and management. The data used in this study was 100% collected from Kafka, showing that when the file size is 4.4 gigabytes, the data processing speed in spark cluster mode is 2 minute 15 seconds, about 3 minutes 19 seconds faster than the local mode.

• Journal of Distribution Science
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• v.15 no.8
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• pp.47-54
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• 2017

Purpose - According to agency theory, managers have incentives to adjust firm revenues to meet earnings expectations or delay bad news disclosure because of performance-based compensation and their reputation in the market. When the bad news accumulates, stock prices fail to reflect all available information. Thus, market prices of stocks are higher than their intrinsic value. After all, bad news crosses the tipping point, it comes out all at once. That results in stock crashes. Auditors can decrease stock crash risk by reducing agency costs through their informational role. Especially, stock price crash risk is expected to be lower for firms adopting high-quality audits. We focus on distribution and service industry to examine the relation between audit quality and stock price crash risk. Industry specialization and auditor size are used as proxies for auditor quality. Research design, data and methodology - Our sample contains distribution and service industry firms listed in KOSPI and KOSDAQ during a period of 2004-2011. We use a logistic regression to test whether auditor quality influences crash risk. Auditor quality was measured by industry specialist auditor and Big4 / non-Big4 dichotomy. Following the approach in prior researches, we use firm-specific weekly returns to measure crash risk. Firms experiencing at least one stock price crash in a specific week during year are classified as the high risk group. Results - The result of analyzing 429 companies in distribution and service industry is summarized as follows: Above all, it is shown that higher audit quality has a significant negative(-) effect on the crash risk. Crash risk is alleviated for firms audited by industry specialist auditors and Big 4 audit firms. Therefore, our results show that hypotheses are supported. Conclusions - This study is very meaningful as the first study which investigated the effects of high audit quality on stock price crash risk. We provide evidence that high-quality auditors reduce stock price crash risk. Our finding implies that the risk of extreme losses can be reduced through screening of high-quality auditors. Therefore investors and regulators may utilize our findings in their investment and rule making decisions.

• Convergence Security Journal
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• v.20 no.4
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• pp.27-34
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• 2020

Recently, the fourth industrial revolution technology has not only changed everyday life greatly through technological development, but has also become a major keyword in the establishment of defense policy. In particular, Internet of Things, cloud, big data, mobile and cybersecurity technologies, called ICBMS, were selected as core leading technologies in defense information policy along with artificial intelligence. Amid the growing importance of the fourth industrial revolution technology, research is being carried out to develop the C4I system, which is currently operated separately by the Joint Chiefs of Staff and each military, including the KJCCS, ATCIS, KNCCS and AFCCS, into an integrated system in preparation for future warfare. This is to solve the problem of reduced interoperability for joint operations, such as information exchange, by operating the C4I system for each domain. In addition, systems such as the establishment of an integrated C4I system and the U.S. military's Risk Management Framework (RMF) are essential for efficient control and safe operation of weapons systems as they are being developed into super-connected and super-intelligent systems. Therefore, in this paper, the intelligent cyber threat detection, management of users' access to information, and intelligent management and visualization of cyber threat are presented in the future C4I system based on big data/cloud.

• Journal of Korea Technology Innovation Society
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• v.16 no.4
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• pp.1163-1186
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• 2013

Big science such as nuclear fusion accelerators shares the characteristic of requiring long-term and massive budget input, human power, and related state-of-the-art technology. Because big science, by nature, thus requires large-scale budgets and facilities yet harbors the possibility of failure, most projects are led by the government. When the actual circumstances are examined, however, such projects are often implemented through the formation of cooperative relations with small and medium businesses (SMBs) possessing outstanding technological capacity. On the other hand, the reality is that the entry of corporations into the business ecosystem of big science is not easy and that even those that have once entered big science likewise fail to find sales outlets for technology that they have developed following the supply of single items, thus leading their technological capacity to lie idle. Consequently, based on an awareness of the problem, the present study seeks to propose strategies for activating the business ecosystem of nuclear fusion and accelerators and to present alternatives regarding which policy tasks must be pursued first by using the analytic hierarchy process (AHP) technique. The present study derived the four policy alternatives of approach, care, expansion, and infrastructures in accordance with the results of empirical analysis to activate the business ecosystem of nuclear fusion and accelerators and analyzed their priority in terms of urgency and effectiveness, the results of which were, in this order: care-approach-expansion-infrastructures. The significance of such research results lie in presenting the policy direction when the government determines which policy task must be pursued first and implements strategies for the activation of the business ecosystem of nuclear fusion and accelerators with limited financial resources in the future.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.384-392
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• 2019

Big data-related research that deals with the amount of explosive information in the era of the Fourth Industrial Revolution is actively underway. Big data is an essential element that promotes the development of artificial intelligence with a wide range of data that become learning data for machine learning, or deep learning. The use of deep learning and big data in various fields has produced meaningful results. In this paper, we have investigated the use of Big Data in the cultural arts industry, focusing on video contents. Noteworthy is that big data is used not only in the distribution of cultural and artistic contents but also in the production stage. In particular, we first looked at what kind of achievements and changes the Netflix in the US brought to the OTT business, and analyzed the current state of the OTT business in Korea. After that, Netflix analyzed the success stories of 'House of Cards', which was produced / circulated through 'Deep Learning' cinematique, which is a prediction algorithm, through accumulated customer data. After that, FGI (Focus Group Interview) was held for cultural and artistic contents experts. In this way, the future prospects of Big Data in the domestic culture and arts industry are divided into technical aspect, creative aspect, and ethical aspect.

• Journal of the Korea Fashion and Costume Design Association
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• v.22 no.3
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• pp.49-61
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• 2020

This study analyzed the social media accounts and performed a Big Data analysis of Z-generation fashion using Textom Text Mining Techniques program and Ucinet Big Data analysis program. The research results are as follows: First, as a result of keyword analysis on 67.646 Z-generation fashion social media posts over the last 5 years, 220,211 keywords were extracted. Among them, 67 major keywords were selected based on the frequency of co-occurrence being greater than more than 250 times. As the top keywords appearing over 1000 times, were the most influential as the number of nodes connected to 'Z generation' (29595 times) are overwhelmingly, and was followed by 'millennials'(18536 times), 'fashion'(17836 times), and 'generation'(13055 times), 'brand'(8325 times) and 'trend'(7310 times) Second, as a result of the analysis of Network Degree Centrality between the key keywords for the Z-generation, the number of nodes connected to the "Z-generation" (29595 times) is overwhelmingly large. Next, many 'millennial'(18536 times), 'fashion'(17836 times), 'generation'(13055 times), 'brand'(8325 times), 'trend'(7310 times), etc. appear. These texts are considered to be important factors in exploring the reaction of social media to the Z-generation. Third, through the analysis of CONCOR, text with the structural equivalence between major keywords for Gen Z fashion was rearranged and clustered. In addition, four clusters were derived by grouping through network semantic network visualization. Group 1 is 54 texts, 'Diverse Characteristics of Z-Generation Fashion Consumers', Group 2 is 7 Texts, 'Z-Generation's teenagers Fashion Powers', Group 3 is 8 Texts, 'Z-Generation's Celebrity Fashions' Interest and Fashion', Group 4 named 'Gucci', the most popular luxury fashion of the Z-generation as one text.

• Information Systems Review
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• v.18 no.1
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• pp.25-39
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• 2016

This study suggests a case for people who are unfamiliar with data analysis to equip them in using big data easily without complex programming tasks. Consequently, we investigate whether a food-themed TV program influences the number of delivery food orders with the use of the difference-in-differences method. Results show that the number of delivery food orders significantly increased after broadcasting four of five food-themed TV program episodes, each of which focuses on a particular delivery food. This study contributes to the existing literature by presenting the possibility that food-themed TV programs can positively affect not only the broadcast delivery food stores but also the entire delivery food business. In addition, this study provides practical contributions by recommending a big data analysis methodology that can be easily employed by many people.