• Proceedings of the Korean Information Science Society Conference
• /
• 2010.06b
• /
• pp.460-464
• /
• 2010

SSD는 뛰어난 성능으로 인해 서버 시장에서 HDD를 빠르게 대체하며 각광받고 있다. 우리는 기존 SSD의 성능 분석이 단일한 I/O 패턴에 대해서만 이루어진 점을 주목하여, 다양한 패턴의 I/O가 동시에 수행 될 경우, 성능에 어떠한 영향이 있는지 평가해보고자 한다. 이를 위해 4KB부터 64MB까지 다양한 블록크기로 순차적/임의적 읽기/쓰기 연산을 수행함과 동시에 4KB 단위의 읽기/쓰기 I/O를 수행시켜 성능에 미치는 영향을 알아보았다. 이러한 평가를 네 가지 리눅스 I/O 스케줄러에 대해 각각 수행함으로써 스케줄러에 의한 영향 또한 평가하였다. 그 결과로 우리는 새로운 SSD의 성능 특성을 발견할 수 있었으며, 이는 새로운 I/O 스케줄러 및 SSD의 FTL 개발의 기반이 되리라 예상된다.

• Information Systems Review
• /
• v.10 no.2
• /
• pp.195-209
• /
• 2008

Customers' needs change every moment. Profitability of stores can't be increased anymore with an existing standardized chain store management. Accordingly, a personalized store management tool needs through prediction of customers' preference. In this study, we propose a recommending procedure using dynamic customers' preference by analyzing the transaction database. We utilize self-organizing map algorithm and association rule mining which are applied to cluster the chain stores and explore purchase sequence of customers. We demonstrate that the proposed methodology makes an effect on recommendation of products in the market which is characterized by a fast fashion and a short product life cycle.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2003.05c
• /
• pp.2125-2128
• /
• 2003

알려진 공격 방법에 대해서는 다양한 방법으로 공격을 탐지하여 적절한 대응을 할 수 있는 반면 알려지지 않은 방법에 의한 공격은 침입탐지 시스템에서 공격 자체를 인식하지 못하므로 적절한 대응을 할 수 없게 된다. 따라서 비정상행위에 대한 탐지를 위해 데이터마이닝 기술을 이용하여 새로운 유형의 공격을 추출하고자 하였다. 특히 대용량의 데이터에 공통적으로 나타나는 순차적인 패턴을 찾는 순차분석 기법 중 PrefixSpan알고리즘을 적용하여 비정상 행위 공격을 탐지할 수 있는 방법을 제시하였다.

• The KIPS Transactions:PartD
• /
• v.16D no.2
• /
• pp.237-248
• /
• 2009

Recently, based on dynamic location or mobility of moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for development of location based services. The performance of moving pattern mining depend on how analyze and process the huge set of spatio-temporal data. Some of traditional spatio-temporal pattern mining methods[1-6,8-11]have proposed to solve these problem, but they did not solve properly to reduce mining execution time and minimize required memory space. Therefore, in this paper, we propose new spatio-temporal pattern mining method which extract the sequential and periodic frequent moving patterns efficiently from the huge set of spatio-temporal moving data. The proposed method reduces mining execution time of $83%{\sim}93%$ rate on frequent moving patterns mining using the moving sequence tree which generated from historical data of moving objects based on hash tree. And also, for minimizing the required memory space, it generalize the detained historical data including spatio-temporal attributes into the real world scope of space and time using spatio-temporal concept hierarchy.

• The Journal of Bigdata
• /
• v.5 no.1
• /
• pp.55-63
• /
• 2020

This study proposed an approach of clustering collective users' activities of location-based social networks using check-in data of Foursquare users in Seoul. In order to cluster the collective activities, we generated sequential rules of the activities using sequential rule mining, and then constructed activity networks based on the rules. We analyzed the activity networks to identify network structure and hub activities, and clustered the activities within the networks. Unlike previous studies that analyzed activity transition patterns of location-based social network users, this study focused on analyzing the structure and clusters of successive activities. Hubs and clusters of activities with the approach proposed in this study can be used for location-based services and marketing. They could also be used in the public sector, such as infection prevention and urban policies.

• Proceedings of the Korean Society for Information Management Conference
• /
• 2001.08a
• /
• pp.213-218
• /
• 2001

본 연구는 육군대학 도서관 홈페이지의 웹서버에 저장되어 있는 로그파일을 실험 데이터로 사용하여, 기존 데이터마이닝(data mining)의 기법들 중에서 연관규칙(association rules) 탐사 기법을 적용함으로써, 사용자들의 웹 항행에 대한 순차패턴을 추출하였다. 이를 분석하여 실제 사용자들이 효과적으로 사용할 수 있는 웹사이트 디자인을 제안하고 나아가 대상 웹사이트의 사용편의성을 평가하였다.

• Journal of Digital Convergence
• /
• v.16 no.6
• /
• pp.85-93
• /
• 2018

The Latent Growth Modeling(LGM) is known as the arising analysis method of longitudinal data and it could be classified into unconditional model and conditional model. Unconditional model requires estimated value of intercept and slope to complete a model of fitness. However, the existing LGM is in absence of a structured methodology to estimate slope when longitudinal data is neither simple linear function nor the pre-defined function. This study used Sequential Pattern of Association Rule Mining to calculate slope of unconditional model. The applied dataset is 'the Youth Panel 2001-2006' from Korea Employment Information Service. The proposed methodology was able to identify increasing fitness of the model comparing to the existing simple linear function and visualizing process of slope estimation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2005.11a
• /
• pp.275-278
• /
• 2005

본 연구는 디스플레이 제품의 개발 및 생산과정에서 OSD 메뉴문자의 오류 유무를 검사하는 과정을 컴퓨터 비전기술을 사용하여 자동화하는 방법을 제안한다. 디스플레이 제품의 OSD 메뉴는 순차적인 제어과정을 통해서 제한된 디스플레이 영역에 여러 종류의 언어와 기호를 포함하는 형태로 출력된다. 기존의 제품개발 과정에서 이러한 메뉴 항목의 정확성을 검증하는 작업은 작업자의 육안에 의한 판단과 수작업에 의해 이루어지고 있는데, 이는 반복작업에 의한 집중력 저하 및 판단착오에 의한 오류의 가능성을 내재한다. 또한 작업자가 다양한 나라의 언어에 대한 문자형태와 기호표현의 특성을 이해하여야 하고, 검증작업 자체에 따르는 부수적인 시간과 노력을 필요로 한다. 이에 본 연구에서는 디스플레이 제품의 OSD 메뉴와 같이 특수한 구조를 갖는 문서영상에 대한 논리적인 구조분석을 통해서 연속적인 문서영상을 발생시키는 작업스케쥴러를 생성하고, 작업스케쥴러에 의해 순차적으로 발생된 영상문서에 대한 전처리, OSD 메뉴의 기하학적 구조분석 및 문자영역을 추출하는 방법과, 표준패턴 구축 및 원형정합에 의한 문자의 오류를 검증하는 방법과 오류를 관리하는 기법을 제안한다.

• Journal of the Economic Geographical Society of Korea
• /
• v.10 no.1
• /
• pp.44-63
• /
• 2007

The purpose of this study is to propose mining processes in the large trip-transaction database of the Metropolitan Seoul area and to analyze the spatial characteristics of travel behavior. For the purpose. this study introduces a mining algorithm developed for exploring trip patterns from the large trip-transaction database produced every day by transit users in the Metropolitan Seoul area. The algorithm computes trip chains of transit users by using the bus routes and a graph of the subway stops in the Seoul subway network. We explore the transfer frequency of the transit users in their trip chains in a day transaction database of three different years. We find the number of transit users who transfer to other bus or subway is increasing yearly. From the trip chains of the large trip-transaction database, trip patterns are mined to analyze how transit users travel in the public transportation system. The mining algorithm is a kind of level-wise approaches to find frequent trip patterns. The resulting frequent patterns are illustrated to show top-ranked subway stations and bus stops in their supports. From the outputs, we explore the travel patterns of three different time zones in a day. We obtain sufficient differences in the spatial structures in the travel patterns of origin and destination depending on time zones. In order to examine the changes in the travel patterns along time, we apply the algorithm to one day data per year since 2004. The results are visualized by utilizing GIS, and then the spatial characteristics of travel patterns are analyzed. The spatial distribution of trip origins and destinations shows the sharp distinction among time zones.

• KIPS Transactions on Software and Data Engineering
• /
• v.2 no.2
• /
• pp.123-130
• /
• 2013

As size of genomic data is increasing rapidly, the needs for high-performance computing system to process and store genomic data is also increasing. In this paper, we captured I/O trace of a system which analyzed 500 million sequence reads data in Genome analysis pipeline for 86 hours. The workload created 630 file with size of 1031.7 Gbyte and deleted 535 file with size of 91.4 GByte. What is interesting in this workload is that 80% of all accesses are from only two files among 654 files in the system. Size of read and write request in the workload was larger than 512 KByte and 1 Mbyte, respectively. Majority of read write operations show random and sequential patterns, respectively. Throughput and bandwidth observed in each processing phase was different from each other.