• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.5 no.4
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• pp.35-49
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• 2010

The purpose of this study is to help the venture business innovate the purchasing process. This study reviewed the types of transaction governance structure, and researched the change of transaction governance structure, while using information technology. According to transaction cost theory, the transaction governance structure can be moved from hierarchy to market. It can be moved to the middle, because of economies of scale, incentive for supplier, and increasing search cost. And it can be moved from market exchange to relational exchange, using electronic marketplace. In order to innovate the purchasing process, the venture business can select the transaction governance structure that fits the its purchasing contexts.

• JSTS:Journal of Semiconductor Technology and Science
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• v.2 no.2
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• pp.132-140
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• 2002

This paper presents an ARM-based SOC bus transaction verification IP and the usage experiences in SOC designs. The verification IP is an AMBA AHB protocol checker, which captures legal AHB transactions in FSM-style signal sequence checking routines. This checker can be considered as a reusable verification IP since it does not change unless the bus protocol changes. Our AHB protocol checker is designed to be scalable to any number of AHB masters and reusable for various AMBA-based SOC designs. The keys to the scalability and the reusability are Object-Oriented Programming (OOP), virtual port, and bind operation. This paper describes how OOP, virtual port, and bind features are used to implement AHB protocol checker. Using the AHB protocol checker, an AHB simulation monitor is constructed. The monitor checks the legal bus arbitration and detects the first cycle of an AHB transaction. Then it calls AHB protocol checker to check the expected AHB signal sequences. We integrate the AHB bus monitor into Verilog simulation environment to replace time-consuming visual waveform inspection, and it allows us to find design bugs quickly. This paper also discusses AMBA AHB bus transaction coverage metrics and AHB transaction coverage analysis. Test programs for five AHB masters of an SOC, four channel DMAs and a host interface unit are executed and transaction coverage for DMA verification is collected during simulation. These coverage results can be used to determine the weak point of test programs in terms of the number of bus transactions occurred and guide to improve the quality of the test programs. Also, the coverage results can be used to obtain bus utilization statistics since the bus cycles occupied by each AHB master can be obtained.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Journal of the Korean Society of Clothing and Textiles
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• v.38 no.6
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• pp.913-928
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• 2014

This study estimated a dual path model to predict consumers' intention to use a QR code virtual store by the effect of a mobile transaction system and a facilitating condition that also examined the role of experience and the use of an intention model in the context of a QR code virtual store. A longitudinal field study was conducted at selected QR code virtual stores. A questionnaire containing mobile transaction system, facilitating condition, performance expectancy, effort expectance, and intention to use was administered at two different points in time: Initial use (T1) and the second use after one month (T2). This study sampled 109 subjects who voluntarily participated in field studies twice at different time points (pooled sample=218). Participants were asked to visit at the QR code virtual store and undertake shopping tasks on their smartphones. The estimated dual path model showed that a mobile transaction system had a positive effect on performance expectancy, which influenced intention to use; however, facilitating condition had a positive effect on effort expectancy, but the effort expectance did not lead to intention to use. The effort expectance significantly also affected the performance expectance influencing intention to use QR code virtual stores. It was also found that use experience moderated the effect of mobile transaction systems on performance expectancy. The findings discussed a critical and success factor in consumer technology acceptance and use over time. A managerial implication was also discussed to capture potential users by emphasizing performance expectancy with the superiority of an innovative system or consumer facilitating condition as external resources in the introduction stage of new technology.

• The Journal of Information Technology
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• v.5 no.1
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• pp.157-171
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• 2002

The evolution of transaction-based business model is upon us. The business models of many e-Marketplace in their early stages have typically been based on transaction fees. Many e-Marketplaces have even called out transaction revenues as a core element of their business plans. The transaction business represents the most simple of business models, but it does not provide a long-term sustain able advantage. For buyer's convenience, wide selection and test price hold appeal. For suppliers, the extended global market reach and direct access to customers and consortiums of customers is powerful. To maxmize leverage of these new e-marketplace, you must from both a buyer perspective as well as a supplier perspective. Also required is a strategy that takes in account all of the various e-Marketplace transaction standards and one that allows the easy accomodation to new e-marketplace as the market change. These new e-marketplace will need to be factored into the sales channel strategies. To be successful, integration with these e-marketplaces should occur at a complete business process level. This study explored independent and industry-backed current and future business models that are emerging in the B2B electronic market industry, as well as value -added service models for the Net market maker industry. E-Marketplaces will evolve into digital work environments in which real industry collaboration can occur.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.143-159
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• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.8
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• pp.269-280
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• 2022

With the rise of a decentralized finance market (so called, DeFi) using blockchain technology, users and capital liquidity of decentralized finance applications are increasing significantly. The Automated Market Maker (AMM) is a protocol that automatically calculates the asset price based on the liquidity of the decentralized trading platform, and is currently most commonly used in the decentralized exchanges (DEX), since it can proceed the transactions by utilizing the liquidity pool of the trading platform even if the buyers and sellers do not exist at the same time. However, Automated Market Maker have some disadvantages since the cost efficiency of each transaction using Automated Market Maker depends on the liquidity size of some liquidity pools used for the transaction, so the smaller the size of the liquidity pool and the larger the transaction size, the smaller the cost efficiency of the trade. To solve this problem, some platforms are adopting Transaction Path Routing Algorithm that bypasses transaction path to other liquidity pools that have relatively large size to improve cost efficiency, but this algorithm can be further improved because it uses only a single transaction path to proceed each transaction. In addition to just bypassing transaction path, in this paper we proposed a Multi-Path Routing Algorithm that uses multiple transaction paths simultaneously by distributing transaction size, and showed that the cost efficiency of transactions can be further improved in the Automated Market Maker-based trading environment.

• Journal of the Korean Regional Science Association
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• v.15 no.3
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• pp.69-86
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• 1999

One of the conclusions from the researches of the last 40 years on the relationship between technological development and economic growth is that technology is an essential factor in economic growth. A majority of firms in developing countries, including Korea, are small to medium sized, so technology transfers is an alternative to developing in those technologies. Such firms prefer technology transfers over self-development because of time and money savings. This paper analyzes the types and processes of technology transfers and suggests a direction and tasks to establish a technomart through an examination of domestic and foreign case studies. Laws, organizations and institutional aspects which are related to establishing technomart are also discussed.

• Asian Journal of Innovation and Policy
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• v.2 no.1
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• pp.97-122
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• 2013

This study secured comparable sales transaction information of technology transfer corresponding to an active market conditions and proposes a method to assess the similarity of technologies with regard to comparability of technology transfer based on these cases information. In order to analyze the association and similarity between target technology and sales transactions, it proposes the significant factors affecting royalty decision and the cosine coefficient method by industry categories. It also proposes the method to adjust royalty, which means that this method unlike the conventional method provides clear standards to valuators in order to revise royalty. Therefore, it offers a solution to the difficulties of applying the market approach for a lot of valuators that have wanted to apply it and objective method to enhance the reliability of the value of intangible asset evaluated by the market approach.