• Journal of Information Processing Systems
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• v.14 no.4
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• pp.837-850
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• 2018

Bitcoin is a decentralized crypto-currency, which is based on the peer-to-peer network, and was introduced by Satoshi Nakamoto in 2008. Bitcoin transactions are written by using a scripting language. The hash value of a transaction's script is used to identify the transaction over the network. In February 2014, a Bitcoin exchange company, Mt. Gox, claimed that they had lost hundreds of millions US dollars worth of Bitcoins in an attack known as transaction malleability. Although known about since 2011, this was the first known attack that resulted in a company loosing multi-millions of US dollars in Bitcoins. Our reason for writing this paper is to understand Bitcoin transaction malleability and to propose an efficient solution. Our solution is a softfork (i.e., it can be gradually implemented). Towards the end of the paper we present a detailed analysis of our scheme with respect to various transaction malleability-based attack scenarios to show that our simple solution can prevent future incidents involving transaction malleability from occurring. We compare our scheme with existing approaches and present an analysis regarding the computational cost and storage requirements of our proposed solution, which shows the feasibility of our proposed scheme.

• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.543-554
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• 2017

We tried to get useful information from social network analysis on the transaction network for the companies in Busan, Ulsan, Kyong-nam region using more than 80 thousand company transaction data obtained from Korean enterprise data (KED). We found that there are 282 clusters having more than 3 companies when we made clusters composed of the companies having the transactions with each other as the first transaction partners in the region. Most of clusters have a major big company with most of sales in the clusters and have the member companies without the transaction with other cluster's member companies so that they have closed and hierarchical transaction pattern. Analysing the transaction network using the network index, we know that there are small medium size companies playing the central role in the regional transaction network and a few clusters have many transactions with other clusters. Also we found that there are very rare sale transactions to the companies outside the region and many purchasing transactions from the companies outside the region. Policy makers need to try to diversify the transaction patterns and to use the exceptional companies and clusters as the levers.

• Journal of Wind Energy
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• v.13 no.4
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• pp.58-69
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• 2022

This study reviews previous studies on the scale of offshore wind power industry ecosystems to provide basic data for a revitalization strategy for the offshore wind power industry and proposes an analysis of transaction networks based on the special classification of the renewable energy industry. First, we examine the localization rate, technology level, and price level of the offshore wind industry. Second, this research compares the methodology and estimation results of previous studies estimating the scale of the wind power industry. Third, we examine the details related to the enactment of a special classification of the renewable energy industry statistics and review the Korea Energy Agency's renewable energy industry statistics (focusing on 2019 and 2020). Finally, this study suggests the necessity of analyzing an inter-company transaction network based on special classifications of the renewable energy industry to grasp the status of each region and value chain of the offshore wind industry.

• Smart Media Journal
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• v.13 no.2
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• pp.108-121
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• 2024

In the IT industry, the complexity and volatility of corporate networks are gradually evolving, and concurrently, the significance of corporate networks is increasing. Previous research has employed network analysis to scrutinize inter-corporate trade relationships for strategic and policy making. However, previous studies focused on the overall network structure from a macroscopic perspective, presenting limitations in applicability at the individual IT corporation level. This study develops a novel research model incorporating sector and region-level network analysis based on connection strength, along with the derivation of a composite node centrality indicator. Using this methodology, we analyzed corporate networks across nine metropolitan areas using IT corporate transaction data. The results means that cities with a manufacturing base, such as Incheon, Busan, and Daegu, have recently established cooperative networks with IT companies. We also found that in the IT industry in Gwangju and Daejeon, certain companies dominate the transaction network.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• KNOM Review
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• v.22 no.3
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• pp.20-24
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• 2019

Blockchain is a distributed ledger-based technology where all nodes participating in the blockchain network are connected to the P2P network. When a transaction is created in the blockchain network, the transaction is propagated and validated by the blockchain nodes. The verified transaction is sent to peers connected to each node through P2P network, and the peers keep the transaction in the memory pool. Due to the nature of P2P networks, the number and type of transactions delivered by a blockchain node is different for each node. As a result, all nodes do not have the same memory pool. Research is needed to solve problems such as attack detection. In this paper, we analyze transactions in the memory pool before solving problems such as transaction fee manipulation, double payment problem, and DDos attack detection. Therefore, this study collects transactions stored in each node memory pool of Bitcoin and Ethereum, a cryptocurrency system based on blockchain technology, and analyzes how much common transactions they have using jacquard similarity.

• Journal of the Korean Society for information Management
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• v.31 no.3
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• pp.89-110
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• 2014

In this study, we analyzed KERIS Document Delivery Service (DDS) using its transaction data for the period of nine years from 2004 to 2012. We first examined the overall statistics focusing on member contributions, and conducted a network analysis based on the records of request/response (supply) between member libraries. Key findings include the following: First, in over 80% of member libraries, the number of outgoing requests exceeded the number of their responses to incoming requests. That is, for the vast majority of member libraries, their participation was concentrated on the request side. Second, KERIS DDS relies heavily on a relatively small number of top contributors, especially on the supply side. While the top contributors were active in both requests and responses (supplies), in most cases, they received and processed a disproportionally large number of requests. Third, the network analysis based on DDS requests for journal articles in 2012 further revealed the central role of top contributors. The level and pattern of concentration, however, appeared to differ by subjects (DDC). Three main patterns of centralization were found in different subjects - a network centered on a single member, a network having multiple centers, or a distributed network.

• The Journal of Economics, Marketing and Management
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• v.6 no.3
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• pp.10-13
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• 2018

Purpose - On the basis of discussing the network economy concept and the commentary of the marginal revenue decreasing of traditional economic theory, The concept of network economy has just been put forward in recent years. The reason why such a concept appears is that the information technology, marked by computer network, plays an increasingly important role in economic activities. Some people define network economy as an economic form based on network technology and human capital. this paper points out network economy existing the marginal revenue increasing and analyzes the reasons that influencing the marginal revenue increasing. Research design, data, methodology - The network economy has fundamentally changed the traditional economic laws. The economic basis of industrial society is the law of incremental marginal cost, which reflects the socialization of high cost in industrial society. Results - As the number of network members increases, the value of the network increases explosively, and the value increases attract more members to join, resulting in more returns. Conclusion - In conclusion, network economy has changed many aspects of traditional economy, resulting in decreasing marginal cost, decreasing transaction cost in and out of enterprise organizations, and making the effect of increasing scale compensation more prominent. This is of great significance to the information construction in China.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.15-20
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• 2023

This study aims to enhance the accuracy of fine dust predictions by analyzing various factors within the local environment, in addition to atmospheric conditions. In the atmospheric environment, meteorological and air pollution data were utilized, and additional factors contributing to fine dust generation within the region, such as traffic volume and electricity transaction data, were sequentially incorporated for analysis. XGBoost, Random Forest, and ANN (Artificial Neural Network) were employed for the analysis. As variables were added, all algorithms demonstrated improved performance. Particularly noteworthy was the Artificial Neural Network, which, when using atmospheric conditions as a variable, resulted in an MAE of 6.25. Upon the addition of traffic volume, the MAE decreased to 5.49, and further inclusion of power transaction data led to a notable improvement, resulting in an MAE of 4.61. This research provides valuable insights for proactive measures against air pollution by predicting future fine dust levels.

• Journal of Information Technology Services
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• v.12 no.3
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• pp.1-21
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• 2013

This study attempts to explain the mechanism behind the migration from vertically integrated value chain architecture to an innovation ecosystem consisting of horizontally separated layers in value chain. We first present a comprehensive framework based on the theoretical analysis of the drivers for migration to an innovation ecosystem, which are standard (institution), capability distribution, and transaction costs. The theoretical framework suggests that the migration to an innovation ecosystem is explained by the influence of standard on the evolutionary change of capability distribution and transaction costs. In particular, when the new de-jure standard competes with the de-facto standard, the new de-jure standard has the greatest impact on the distribution capabilities and the transaction costs. Based on this theoretical framework, we analyze the latest SDN (Software Defined Networking) case of the network industry. SDN standard has transformed the industry from a vertically integrated value chain architecture to a horizontally separated one with its influence on the distribution capabilities and the transaction costs in the industry.