• Journal of Information Processing Systems
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• 제13권6호
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• pp.1575-1584
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• 2017

As interest in big data has increased recently, NoSQL, a solution for storing and processing big data, is getting attention. NoSQL supports high speed, high availability, and high scalability, but is limited in areas where data integrity is important because it does not support multiple row transactions. To overcome these drawbacks, many studies are underway to support multiple row transactions in NoSQL. However, existing studies have a disadvantage that the number of transactions that can be processed per unit of time is low and performance is degraded. Therefore, in this paper, we design and implement a multi-row transaction system for data integrity in big data environment based on HBase, a column-based NoSQL which is widely used recently. The multi-row transaction system efficiently performs multi-row transactions by adding columns to manage transaction information for every user table. In addition, it controls the execution, collision, and recovery of multiple row transactions through the transaction manager, and it communicates with HBase through the communication manager so that it can exchange information necessary for multiple row transactions. Finally, we performed a comparative performance evaluation with HAcid and Haeinsa, and verified the superiority of the multirow transaction system developed in this paper.

• 정보처리학회논문지D
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• 제9D권2호
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• pp.251-258
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• 2002

대용량의 데이터들로부터 사용자가 인하는 데이터를 찾기 위하여 많은 데이터 마이닝 기술들이 연구되어 실제 응용프로그램에서 많이 적용되고 있다. 이러한 데이터 마이닝 기술들은 시계열 데이터를 이용하는 경우보다 트랜잭션 데이터를 이용하여 유용한 정보를 찾는 경우에 초점이 맞춰져 있다. 본 논문에서는 시계열 데이터를 트랜잭션 데이터로 변환하는 접근방법을 소개한다. 가상 트랜잭션은 서로 상대적으로 근접한 시간에 발생하는 이벤트의 집합이라고 정의하며, 가상 트랜잭션 생성기는 가상 트랜잭션을 생성시 시간윈도우와 이벤트 윈도우 방법을 사용한다. 본 논문의 접근 방법을 사용하여 기존의 트랜잭션 데이터를 이용하는 많은 데이터 마이닝 알고리즘들을 수정 없이 시계열 데이터에 적용하여 유용한 정보를 찾을 수 있다.

• 지식경영연구
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• 제21권1호
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• pp.117-136
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• 2020

In the studies for the recommender systems which solve the information overload problem of users, the use of transactional data has been continuously tried. Especially, because the firms can easily obtain transactional data along with the development of IoT technologies, transaction-based recommender systems are recently used in various areas. However, the use of transactional data has limitations that it is hard to reflect domain knowledge and they do not directly show user preferences for individual items. Therefore, in this study, we propose a method applying the word embedding in the transaction-based recommender system to reflect preference differences among users and domain knowledge. Our approach is based on SAR, which shows high performance in the recommender systems, and we improved its components by using FastText, one of the word embedding techniques. Experimental results show that the reflection of domain knowledge and preference difference has a significant effect on the performance of recommender systems. Therefore, we expect our study to contribute to the improvement of the transaction-based recommender systems and to suggest the expansion of data used in the recommender system.

• 한국정보과학회논문지:데이타베이스
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• 제31권3호
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• pp.297-306
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• 2004

무선 정보 서비스에서 푸쉬-기반 브로드캐스팅은 데이타 항목의 개수가 작은 경우 많은 수의 클라이언트에게 정보를 확산시키는 매우 효과적인 기술이다. 그렇지만, 데이타베이스 용량이 큰 경우에는 풀-기반의 (클라이언트에서 서버로의) 역채널을 푸쉬-기반의 브로드캐스트와 결합한 이른바 혼합 데이터 전송이 유리할 수 있다. 본 논문은 순수 푸쉬-기반 데이타 브로드캐스트 환경에서 제시되었던 기선언-기반 트랜잭션 처리 기법을 혼합 데이타 전송에 적용하고, 시뮬레이션을 통해 그 성능을 분석한다. 시뮬레이션 결과를 통해, 기선언-기반 트랜잭션 처리 기법이 순수 푸쉬 데이타 전송뿐만 아니라 혼합 데이타 전송에서도 우수한 성능을 나타냄을 알 수 있다.

• International Journal of Contents
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• 제12권1호
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• pp.60-64
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• 2016

As new applications of cloud data management system (CDMS) such as online games, cooperation edit, social network, and so on, are increasing, transaction processing capabilities for CDMS are required. Several transaction processing methods for cloud data management system (CDMS) have been proposed. However, existing transaction processing methods have some problems. Some of them provide limited transaction processing capabilities. Some of them are hard to be integrated with existing CDMSs. In this paper, we proposed a new concurrency control method to support transaction processing capability for CDMS to solve these problems. The proposed method was designed and implemented based on Spark, an in-memory distributed processing framework. It uses RDD (Resilient Distributed Dataset) model to provide fault tolerant to data in the main memory. In our proposed method, database stored in CDMS is loaded to main memory managed by Spark. The loaded data set is then transformed to RDD. In addition, we proposed a multi-version concurrency control method through immutable characteristics of RDD. Finally, we performed experiments to show the feasibility of the proposed method.

• 대한산업공학회지
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• 제42권4호
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• pp.249-255
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• 2016

Recently, transaction data is accumulated everywhere very rapidly. Association analysis methods are usually applied to analyze transaction data, but the methods have several problems. For example, these methods can only consider one-way relations among items and cannot reflect domain knowledge into analysis process. In order to overcome defect of association analysis methods, we suggest a transaction data analysis method based on probabilistic graphical model (PGM) in this study. The method we suggest has several advantages as compared with association analysis methods. For example, this method has a high flexibility, and can give a solution to various probability problems regarding the transaction data with relationships among items.

• 한국국방경영분석학회지
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• 제22권1호
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• pp.81-96
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• 1996

In a relational database environment, partitioning of data is directly concerned with the amount of data that needs to be required in a query or transaction. In this paper, we consider non-overlapping, vertical partitioning. Vertical partitioning algorithm in this paper is composed of two phases. In phase 1, we cluster the attributes with zero-one integer program that maximize affinity among attributes. The result of phase 1 is called 'Initial Fragments'. In phase 2, we modify Initial Fragments that is not directly considered by cost factors, making use of a transaction-based partitioning method. A transaction-based partitioning method is partitioning attributes according to a set of transactions. In this phase we select logical accesses which needs to be required in a transaction as comparison criteria. In phase 2, proposed algorithm consider only small number of modification of Initial Fragments in phase 1. This algorithm is so insensible to number of transactions and of attributes that it can applied to relatively large problems easily.

• ETRI Journal
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• 제39권1호
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• pp.135-144
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• 2017

Recently, mobile phones have been recognized as the most convenient type of mobile payment device. However, they have some security problems; therefore, mobile devices cannot be used for unauthorized transactions using anonymous data by unauthenticated users in a cloud environment. This paper suggests a mobile payment system that uses a certificate mode in which a user receives a paperless receipt of a product purchase in a cloud environment. To address mobile payment system security, we propose the transaction certificate mode (TCM), which supports mutual authentication and key management for transaction parties. TCM provides a software token, the transaction certificate token (TCT), which interacts with a cloud self-proxy server (CSPS). The CSPS shares key management with the TCT and provides simple data authentication without complex encryption. The proposed self-creating protocol supports TCM, which can interactively communicate with the transaction parties without accessing a user's personal information. Therefore, the system can support verification for anonymous data and transaction parties and provides user-based mobile payments with a paperless receipt.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2004년도 추계학술대회 및 정기총회
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• pp.68-98
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• 2004

Although the internet is useful for transferring information, Internet auction environments make fraud more attractive to offenders because the chance of detection and punishment are decreased. One of fraud is phantom transaction which is a colluding transaction by the buyer and seller to commit illegal discounting of credit card. They pretend to fulfill the transaction paid by credit card, without actual selling products, and the seller receives cash from credit card corporations. Then seller lends it out buyer with quite high interest rate whose credit score is so bad that he cannot borrow money from anywhere. The purpose of this study is to empirically investigate the factors to detect of the phantom transaction in online auction. Based up on the studies that explored behaviors of buyers and sellers in online auction, bidding numbers, bid increments, sellers' credit, auction length, and starting bids were suggested as independent variables. We developed an Internet-based data collection software agent and collect data on transactions of notebook computers each of which winning bid was over 1,000,000 won. Data analysis with logistic regression model revealed that starting bids, sellers' credit, and auction length were significant in detecting the phantom transaction.

• 한국IT서비스학회지
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• 제18권2호
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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.