• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.4
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• pp.324-341
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• 2023

Topological data analysis (TDA) is a data analysis technique, recently developed, that investigates the overall shape of a given dataset. The mapper algorithm is a TDA method that considers the connectivity of the given data and converts the data into a mapper graph. Compared to persistent homology, another popular TDA tool, that mainly focuses on the homological structure of the given data, the mapper algorithm is more of a visualization method that represents the given data as a graph in a lower dimension. As it visualizes the overall data connectivity, it could be used as a prediction method that visualizes the new input points on the mapper graph. The existing mapper packages such as Giotto-TDA, Gudhi and Kepler Mapper provide the descriptive mapper algorithm, that is, the final output of those packages is mainly the mapper graph. In this paper, we develop a simple predictive algorithm. That is, the proposed algorithm identifies the node information within the established mapper graph associated with the new emerging data point. By checking the feature of the detected nodes, such as the anomality of the identified nodes, we can determine the feature of the new input data point. As an example, we employ the fraud credit card transaction data and provide an example that shows how the developed algorithm can be used as a node prediction method.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.143-161
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• 2022

This study examines the effect of used trading app's consumption value and protection motivation and the perceived usefulness and continuous use intention. The proposed research model and developed hypotheses were tested using structural equations modeling based on data collected from 293 customers with experience in used transaction app's. The results of the study confirm the positive effects of the used trading app's consumption value and protection motive theory is perceived usefulness of customer. In addition, there is a positive relationship between a customer's perceived usefulness and continuous use intention of used trading app's. The study provides On a theoretical level valuable insights into the sustainability of transaction app's after post-COVID 19 and the importance of developing used trading app's consumption value and protection motivation, but there is also a limitation that the region is limited.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.11C
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• pp.1064-1073
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• 2002

In this paper, we design and implement the Wireless Transaction Protocol (WTP) proposed by the Wireless Application Protocol (WAP) forum using a protocol development tool, SDL Development Tool (SDT). And we conduct a comparative performance evaluation of the WTP implementation with other three implementations that are based on different implementation models respectively: the server model, the coroutine model, and the activity-thread model. To implement WTP, we first use Unified Modeling Language (UML) for analyzing the protocol requirement and defining the protocol engine architecture. Next, we use Software Development Language (SDL) to design the protocol engine in details and then generate the WTP implementation automatically with the aid of SDT The code size of the WTP implementation generated by SDT is 62% larger than the other three implementations. However, its throughput and system response time for transaction processing is almost equal to the other three implementations when the number of concurrent clients is less than 3,000. If more than 5,000 concurrent clients tries, the transaction success rate abruptly decreases to 10% and system response time increases to 1,500㎳, due to the increased protocol processing time. But, it comes from the fact that the load overwhelms the capacity of the PC resource used in this experimentation.

• Journal of KIISE:Databases
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• v.29 no.4
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• pp.297-310
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• 2002

The 2PL-HP(Two Phase Locking with High Priority) method has been used to guarantee preceding process of a high priority transaction(HPT) in real-time database systems. The method resolves a conflict through aborting or blocking of a low priority transaction(LPT). However, if HPT is eliminated in a system because of its deadline missing, an unnecessary aborting or blocking of LPT is occurred. Recently, to resolve the problem, a concurrency control algorithm using alternative version was proposed. However, the algorithm must always create the alternative version and needs an addtional technique to manage complex alternative versions. In this paper, we propose an efficient concurrency control algorithm that prevents needless wastes of resources and eliminates unnecessary aborting or blocking of LTP. And it is shown through the performance evaluation that the proposed concurrency control algorithm outperforms the existing concurrency control algorithm using alternative version.

• The Korean Journal of Franchise Management
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• v.12 no.4
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• pp.1-11
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• 2021

Purpose: The franchise system started by Singer Sewing Machine in the US is acting as a national economic growth engine in terms of job creation and economic growth. In China, the franchise system was introduced in the mid-1980s. And since joining the WTO, it has grown by 5-6% every year. However, compared to the growth rate of franchises, studies on shared growth between the chain headquarters and franchisees were insufficient. Accordingly, recent studies related to shared growth between the chain headquarters and franchisees have been active in China. The purpose of this study is to examine the knowledge transfer system between the knowledge creation, knowledge sharing, and the use of knowledge by franchise chain headquarters in China. In addition, the relationship between franchise satisfaction and performance is identified. Research design, data, and methodology: The data were collected from franchise stores in Sichuan, China, and were conducted with the help of ○○ Incubation, a Sichuan Province-certified incubator. From November 2020 to January 2021, 350 copies of the questionnaire were distributed in China, and 264 copies were returned. Of these, 44 copies with insincere answers and response errors were excluded, and 222 copies were used for analysis. The data were analyzed with SPSS 22.0 and AMOS 22.0 statistical packages. Result: The results of this study are as follows. First, knowledge creation has been shown to have a statistically significant impact on knowledge sharing and knowledge utilization. In particular, the effectiveness of knowledge creation was higher in knowledge sharing than in knowledge utilization. And we can see that knowledge sharing also has a statistically significant e ffect on knowledge utilization. Second, knowledge sharing was not significant for transaction satisfaction and business performance, and knowledge utilization was significant for transaction satisfaction and business performance. These results can be said to mean less interdependence of the Chinese franchise system. Finally, transaction satisfaction was statistically significant to business performance. The purpose of this study was to examine the importance of knowledge management to secure long-term competitive advantage for Chinese franchises. This study shows that knowledge sharing is important for long-term franchise growth. And we can see that there is a lack of knowledge sharing methods in the case of franchises in China. I n addition, it was found that the growth of Chinese franchises requires systematization of communication, information sharing measures and timing, help from chain headquarters, and mutual responsibility awareness.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.590-596
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• 2020

In the island areas where system connection with the commercial power grid is difficult, it is quite important to find a method to efficiently manage energy produced with independent microgrids. In this paper, a prosumer management system for P2P power transaction was realized through the testing the power meter and the response rate of the collected data for the power produced in the small-scale microgrids in which hybrid models of solar power and wind power were implemented. The power network of the microgrid prosumer was composed of mesh structure and the P2P power transaction was tested through the power meter and DC power transmitter in the off-grid sites which were independently constructed in three places. The measurement values of the power meter showed significant results of voltage (average): 380V + 0.9V, current (average): + 0.01A, power: 1000W (-1W) with an error range within ±1%. Stabilization of the server was also confirmed with the response rate of 0.32 sec. for the main screen, 2.61 sec. for the cumulative power generation, and 0.11 sec for the power transaction through the transmission of 50 data in real time. Therefore, the proposed system was validated as a P2P power transaction system that can be used as an independent network without transmitted by Korea Electric Power Corporation (KEPCO).

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.169-179
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• 2024

Currently, information technologies such as blockchain and metaverse are being innovatively developed in Korea and around the world. The government has defined the innovation of these cyber-related technologies as the fourth industrial revolution and presented the Digital New Deal as an important policy of the Korean version of the New Deal, and is implementing various policies and systems related to it. This situation is expected to affect the development of the real estate registration system in Korea. Moreover, as the Supreme Court is currently promoting the transition to a future registration system, it is necessary to examine whether blockchain technology, which allows parties to exchange value without a third party guaranteeing the transaction, can be used in the real estate registration system. In order to secure the credibility of the real estate registration as electronic information under the registration system that introduces electronic registration and blockchain system, the transparency of transaction identification and real estate registration details should also be recorded using the blockchain system as a way to prevent such crimes and legal disputes. As a solution, it is worth considering how to improve the reliability of transaction identification, recognize the actual examination rights of the registrar in the foundation system of the real estate register, and increase public trust by going through the notarization stage when recording rights such as real rights, and consider how to introduce a blockchain system at this stage to ensure integrity and reliability. In the stage before the current real estate registration and study system is converted to a blockchain system, the clarity, transparency, and consistency of the real estate registration entries with the actual real estate must be established so that the real estate study can finally be recognized as authoritative, thereby ensuring the trust of the transaction parties to the real estate study system that has adopted the blockchain system in the future, and bringing us closer to the goal of real estate transactions in the form of smart contracts between the parties who have trusted it based on transparency and integrity of real estate study in the real estate transaction market.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Journal of KIISE:Databases
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• v.31 no.5
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• pp.516-526
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• 2004

Main-Memory DataBase(MMDB) system where all the data reside on the main memory shows tremendous performance boost since it does not need any disk access during the transaction processing. Since MMDB still needs disk logging for transaction commit, it has become another bottleneck for the transaction throughput and the commit protocol should be examined carefully. There have been several attempts to reduce the logging overhead. The pre-commit and group commit are two well known techniques which do not require additional hardware. However, there has not been any research to analyze their effect on MMDB system. In this paper, we identify the possibility of deadlock resulting from the group commit and propose the disk group commit protocol which can be readily deployed. Using extensive simulation, we have shown that the group commit is effective on improving the MMDB transaction performance and the proposed disk group commit almost always outperform carefully tuned group commit. Also, we note that the pre-commit does not have any effect when used alone but shows some improvement if used in conjunction with the group commit.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.65-77
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• 2022

Due to the recent development in electronic financial services, transactions of electronic prepayment are rapidly growing, leading to growing fraud attempts. This paper proposes a methodology that can effectively detect fraud transactions in electronic prepayment by machine learning algorithms, including support vector machines, decision trees, and artificial neural networks. Actual transaction data of electronic prepayment services were collected and preprocessed to extract the most relevant variables from raw data. Two different approaches were explored in the paper. One is a transaction-based approach, and the other is a user ID-based approach. For the transaction-based approach, the first model is primarily based on raw data features, while the second model uses extra features in addition to the first model. The user ID-based approach also used feature engineering to extract and transform the most relevant features. Overall, the user ID-based approach showed a better performance than the transaction-based approach, where the artificial neural networks showed the best performance. The proposed method could be used to reduce the damage caused by financial accidents by detecting and blocking fraud attempts.