• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.127-133
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• 2019

Since frequent item extraction so far requires searching for patterns and traversal for the FP-Tree, it is more likely to store the mining data in a tree and thus CPU time is required for its searching. In order to overcome these drawbacks, in this paper, we provide each item with its location identification of transaction data without relying on conditional FP-Tree and convert transaction data into 2-dimensional position information look-up table, resulting in the facilitation of time and spatial accessibility. We propose an algorithm that considers the mapping scheme between the location of items and items that guarantees the linear time complexity. Experimental results show that the proposed method can reduce many execution time and memory usage based on the data set obtained from the FIMI repository website.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.715-729
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• 2021

Bitcoin is one of the cryptocurrencies, which is decentralized and transparent. However, due to its anonymity, it is currently being used for the purpose of transferring funds for illegal transactions in darknet markets. To solve this problem, clustering heuristic based on the characteristics of a Bitcoin transaction has been proposed. However, we found that the previous heuristis suffer from high false negative rates. In this study, we propose a novel heuristic for bitcoin clustering using off-chain data. Specifically, we collected and analyzed user review data from Silk Road 4 as off-chain data. As a result, 31.68% of the review data matched the actual Bitcoin transaction, and false negatives were reduced by 91.7% in the proposed method.

• Journal of the Economic Geographical Society of Korea
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• v.23 no.1
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• pp.1-17
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• 2020

The study aims to identify economic interdependencies between regions and define functional economic areas of Korea by analyzing inter-firm transaction networks. Previous research has relied on pre-given administrative boundaries or cultural homogeneity and used data such as commuting, population movement, and cargo flows which could not fully explain economic activities. To overcome the limitations, this study applies a community detection method to inter-firm transaction networks derived from the CRETOP+ database of Korean corporate data. The novel dataset and the network analysis enables us to identify Korea's functional economic areas based on actual inter-firm linkages. The result shows that there are six to seven economic blocs in the networks as of 2018. In particular, one huge economic bloc is formed integrating the Seoul metropolitan area, Chungcheong, and Gangwon provinces. Meanwhile, North Jeolla and South Jeolla provinces form two economic blocs separately rather than being tied up in one bloc due to the low frequency of transactions between each other. The two big economic blocs of Daegu-Gyeongbuk and Busan-Gyeongnam exist, and interestingly, Ulsan, Gyeongju, and Pohang form a separate middle-sized bloc across the administrative boundaries. The results reveal that the future balanced national development policies should be implemented based on functional economic areas derived from empirical data.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.458-467
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• 2001

Optimistic Two-Phase Locking(O2PL) performs as well as or better than the other approaches because it exploits client caching well and also has relatively lower network bandwidth requirements. However, O2PL leads to unnecessary waits, because, it can not be commit a transaction until the transaction obtains all requested locks. In addition, Optimistic Concurrency Control(OCC) tends to make needless aborts. This paper suggests an efficient optimistic cache consistency protocol that overcomes such shortcomings. Our scheme decides whether to commit or abort a transaction without wait and it adopts transaction re-ordering in order to minimize the abort rate. Our scheme needs only one version for each data item in spite of the re-ordering mechanism used. Finally, this paper presents a simulation-based analysis that shows superiority in performance of out scheme to O2PL and OCC.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.397-407
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• 2003

Transactions of updating spatial dta with mobile clients are log transactions because a user disconnected from a server surveys real features and updates them. In this environment, it is appropriate to exploit the optimistic approach based on the validation test in order to control the concurrency of transactions. On the contrary, the pessimistic concurrency control scheme makes transactions wait for a long time due to the lock. In this paper, we propose the surrogate transaction model and implement its manager for the S-S-M(Server-Surrogate PC-Mobile Client) structure which is appropriate for updating spatial data in mobile environments. In the S-S-M structure, the mobile client communicates with the server by the surrogate PC. We extend the validation condition in consideration of spatial relationships between spatial objects in this model. We also present the commit protocol where the user of a surrogate PC adjusts objects of the conflicted surrogate transaction to minimize costs for the abortion of the transaction.

• Journal of Information Technology Applications and Management
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• v.29 no.6
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• pp.43-62
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• 2022

As the used trading market grows centered on online platforms, it is evolving from practical consumption to pursuing various forms of consumption value. Consumers purchase rare products, enjoy the transaction itself with consumers with the same preference, and investment or eco-friendly value consumption is also increasing. In this regard, the transaction of the second-hand transaction app service must have sincerity in order to respond to various consumption values. In addition, it is necessary to study what components the second-hand trading app service should aim for to secure authenticity and how the usefulness and ease of second-hand trading apps affect the relationship. The research used a questionnaire method to test hypotheses and surveyed 215 consumers in their 20s, 30s, and 40s who had purchased used clothing online within a year. Among them, 200 responses, excluding outliers, were statistically analyzed using SPSS 21.0 and AMOS 22.0 as confirmatory factor analysis and structural equation models. This study aims to determine the effect of the authenticity of the online used trading app service on the satisfaction of the used trading app service. To this end, based on the previous research results, the following research hypotheses were established and verified statistically. First, the authenticity of the online used trading app service will significantly impact the satisfaction of the used trading app service. Second, the usefulness of the used trading app service will have a moderating effect on the effect of the authenticity of the used trading app service on the satisfaction of the used trading app service. Third, the ease of the used trading app service will have a moderating effect on the authenticity of the used trading app service on the satisfaction of the used trading app service. As a result of the study, the authenticity of the used trading app service significantly affected the satisfaction of the used trading app service. Appeared to be The results of this study will help understand used transaction app services where consumption of used products occurs from the viewpoint of product circular use behavior and can be used as a strategic plan to promote the use of used transaction apps in the future.

• Journal of Information Technology Applications and Management
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• v.26 no.1
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• pp.65-75
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• 2019

As the use of semantic web based on XML increases in the field of data management, a lot of studies to extract useful information from the data stored in ontology have been tried based on association rule mining. Ontology data is advantageous in that data can be freely expressed because it has a flexible and scalable structure unlike a conventional database having a predefined structure. On the contrary, it is difficult to find frequent patterns in a uniformized analysis method. The goal of this study is to provide a basis for extracting useful knowledge from ontology by searching for frequently occurring subgraph patterns by applying transaction-based graph mining techniques to ontology schema graph data and instance graph data constituting ontology. In order to overcome the structural limitations of the existing ontology mining, the frequent pattern search methodology in this study uses the methodology used in graph mining to apply the frequent pattern in the graph data structure to the ontology by applying iterative node chunking method. Our suggested methodology will play an important role in knowledge extraction.

• The KIPS Transactions:PartC
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• v.10C no.7
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• pp.857-866
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• 2003

For detecting an intrusion based on the anomaly of a user's activities, previous works are concentrated on statistical techniques in order to analyze an audit data set. However. since they mainly analyze the average behavior of a user's activities, some anomalies can be detected inaccurately. In this paper, a new clustering algorithm for modeling the normal pattern of a user's activities is proposed. Since clustering can identify an arbitrary number of dense ranges in an analysis domain, it can eliminate the inaccuracy caused by statistical analysis. Also, clustering can be used to model common knowledge occurring frequently in a set of transactions. Consequently, the common activities of a user can be found more accurately. The common knowledge is represented by the occurrence frequency of similar data objects by the unit of a transaction as veil as the common repetitive ratio of similar data objects in each transaction. Furthermore, the proposed method also addresses how to maintain identified common knowledge as a concise profile. As a result, the profile can be used to detect any anomalous behavior In an online transaction.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.517-524
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• 2020

The study examines factors affecting apartment prices in the real estate market of Ho Chi Minh City, Vietnam. The study uses primary data based on surveys of customers who have traded successfully, and collects transaction data from real estate trading companies that are the top investors in Ho Chi Minh City real estate market. The collected data include 384 observations in a total of 24 districts, detailing that each district surveyed on a minimum of four projects, each project carried out a survey on a minimum of four apartments. The survey collected 339 valid questionnaires for analysis and model testing. This study employs multivariate regression with the data of 339 observations. The research results reveal that five significant factors affect positively the price of apartments in Ho Chi Minh City - apartment area, toilet and bedroom, apartment floor, reference price, and apartment interior. Besides, there are three significant factors affecting negatively the price of apartments - next price trend, distance to city center, and potential building. From the results, the research proposes solutions in the pricing of apartments in the real estate market in Ho Chi Minh City - better information system, a real estate transaction index, and stricter management of small brokerage activities.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1143-1148
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• 2004

To develop multi-tier model or distributed architecture based distributed application needs to consider various aspects such as division of role between web-designer and software developer, defining entity and its usage, database connection and transaction processing etc. This paper presents DONSL(Data Server of Non SQL-Query) architecture that provides solution to above aspects through web-tier object modeling. This is the architecture that guarantees the transaction processing and performance between web-tier and DBMS through simplified usage of query logic property. This new conceptual framework also solves enterprise site implementation problems simplifying tier, and removing DAO(Data Access Object) and entity.