To identify the cause of the error and maintain the health of system, an administrator usually analyzes event log data since it contains useful information to infer the cause of the error. However, because today’s systems are huge and complex, it is almost impossible for administrators to manually analyze event log files to identify the cause of an error. In particular, as OpenStack, which is being widely used as cloud management system, operates with various service modules being linked to multiple servers, it is hard to access each node and analyze event log messages for each service module in the case of an error. For this, in this paper, we propose a novel message-based log analysis method that enables the administrator to find the cause of an error quickly. Specifically, the proposed method 1) consolidates event log data generated from system level and application service level, 2) clusters the consolidated data based on messages, and 3) analyzes interrelations among message groups in order to promptly identify the cause of a system error. This study has great significance in the following three aspects. First, the root cause of the error can be identified by collecting event logs of both system level and application service level and analyzing interrelations among the logs. Second, administrators do not need to classify messages for training since unsupervised learning of event log messages is applied. Third, using Dynamic Time Warping, an algorithm for measuring similarity of dynamic patterns over time increases accuracy of analysis on patterns generated from distributed system in which time synchronization is not exactly consistent.

상수도관의 파열은 과도한 압력, 노후화, 온도변화나 지진 등에 의한 지반이동에 의해 발생한다. 상수도관 파열이 대규모 단수, 싱크홀 등과 같은 더 심각한 피해 이어지지 않도록 신속하게 탐지 및 대응하는 것이 중요하다. 본 연구에서는 상수도관 파열 탐지를 위해 개선 Western Electric Company (WECO) 방법을 개발하였다. 개선 WECO 방법은 통계적공정관리기법 중 하나인 기존 WECO 방법에 임계치 조정자(w)를 추가하여 대상 네트워크에 적합한 이상탐지 의사결정을 할 수 있도록 했다. 개발된 개선 WECO 방법을 미국 텍사스 오스틴 관망에 적용 및 검증하였다. 상수도관 파열 발생 시 측정한 비정상데이터와 수요량 변동만 고려한 정상데이터를 이용하여 기존 및 개선 WECO 방법을 비교하였다. 최적 임계치 조정자 w값을 결정하기 위해 민감도 분석을 수행하였으며, 다양한 계측시간 간격 데이터(dt = 5, 10, 15분 등)의 영향도 분석하였다. 각 경우 별 탐지 성능은 탐지확률, 오경보확률, 평균탐지시간을 계산하여 비교하였다. 본 연구에서는 도출된 결과를 바탕으로 WECO 방법을 실제 상수도관 파열 탐지에 적용하기 위한 가이드라인을 제공한다.

Transgenic plants that over express virus coat protein genes have attracted particular interest from researchers, by virtue of their tolerance to virus infection. The transgenic watermelon rootstock analyzed in this study was established by introducing CGMMV coat protein (cp) under the control of CaMV 35S promoter and NOS terminator (Park et al., (2005) Plant Cell Rep. 24: 350-6). The primary objective of this study was to determine the copy number and integration site of the transgene element, in order to develop detection techniques required for monitoring of the transgenic watermelon rootstock. The Southern blot analysis indicated that a single copy of CGMMV-cp gene was inserted into the genome of transgenic watermelon rootstock. We also identified the genomic sequences flanking the integration site of the transgene by inverse PCR analysis. In an effort to find a sequence usable as an internal positive control for the screening of the watermelon and watermelon rootstock, we found that the Sat and DIP-1 genes appears as one copy within their genomes and is watermelon rootstock- and watermelon-specific. The information of the integrated site and the internal positive control sequence was used to establish a new event-specific PCR-based detection method. In addition, mRNA and protein expression level of the transgene in the transgenic watermelon rootstock and grafted watermelon were investigated. The expression of both mRNA and protein of CGMMV-CP was not detected in the transgenic watermelon rootstocks and watermelons, suggesting that the movement of transgene products from transgenic rootstock to watermelon does not occur at our detection level.