• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.21-28
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• 2019

Process mining is state-of-the-art technology in the workflow field. Recently, process mining becomes more important because of the fact that it shows the status of the actual behavior of the workflow model. However, as the process mining get focused and developed, the material of the process mining - workflow event log - also grows fast. Thus, the process mining algorithms cannot operate with some data because it is too large. To solve this problem, there should be a lightweight process mining algorithm, or the event log must be divided and processed partly. In this paper, we suggest a set of operations that control and edit XES based event logs for process mining. They are designed based on relational algebra, which is used in database management systems. We designed three operations for tailoring XES event logs. Select operation is an operation that gets specific attributes and excludes others. Thus, the output file has the same structure and contents of the original file, but each element has only the attributes user selected. Union operation makes two input XES files into one XES file. Two input files must be from the same process. As a result, the contents of the two files are integrated into one file. The final operation is a slice. It divides anXES file into several files by the number of traces. We will show the design methods and details below.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.537-541
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• 2008

Log-Structured File System(LFS) is a disk based file system that is optimized for improving the write performance. LFS gathers dirty data in memory as long as possible, and flushes all dirty data sequentially at once. In a real system, however, maintaining dirty data in memory should be flushed into a disk to meet file system consistency issues even if more memory is still available. This synchronizations increase the cleaner overhead of LFS and make LFS to write down more metadata into a disk. In this paper, by adapting Non-volatile RAM(NV-RAM) we modifies LFS and virtual memory subsystem to guarantee that LFS could gather enough dirty data in the memory and reduce small disk writes. By doing so, we improves the performance of LFS by around 2.5 times than the original LFS.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.88-93
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• 2005

Web usage mining is a research field for searching potentially useful and valuable information from web log file. Web log file is a simple list of pages that users refer. Therefore, it is not easy to analyze user's current interest field from web log file. This paper presents web usage mining method for finding users' current interest based on fuzzy categories. We consider not only how many times a user visits pages but also when he visits. We describe a user's current interest with a fuzzy interest degree to categories. Based on fuzzy categories and fuzzy interest degrees, we also propose a method to cluster users according to their interests for user modeling. For user clustering, we define a category vector space. Experiments show that our method properly reflects the time factor of users' web visiting as well as the users' visit number.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06b
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• pp.310-314
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• 2007

Log-Structured File system은 쓰기에 최적화한 파일 시스템으로 변경된 데이터를 최대한 모아서 순차적으로 기록하는 방식을 가지고 있다. 그러나 실제 시스템에서는 주기적인 동기화로 인해 작은 크기의 데이터들이 디스크로 쓰여지게 되면서 원래의 디자인 목표를 살리지 못하게 된다. 본 연구에서는 최근 급속도로 발전하고 있는 비휘발성 메모리(NVRAM)를 이용해서 주기적인 동기화를 없애고 작은 단위의 쓰기는 NVRAM을 통해 흡수하도록 하였다. 이를 통하여 DRAM만 있는 LFS에 비해 33% 가량 TPC-C 수행 성능이 향상되고, 더 빠르고 고른 응답 시간을 보일 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.645-648
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• 2017

The existing embedded tester used TCP and shared file system for log processing. In addition, the existing processing method was treated as 1-N structure. This method wastes resources of the tester for exception handling. We implemented a log processing message layer that can be distributed by messaging system. And we compare the transmission method using the message layer and the transmission method using TCP and the shared file system. As a result of comparison, transmission using the message layer showed higher transmission bandwidth than TCP. In the CPU usage, the message layer showed lower efficiency than TCP, but showed no significant difference. It can be seen that the log processing using the message layer shows higher efficiency.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2442-2450
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• 1999

The audit logging system is to write the details of systems use and access on networks. These details are used for trailing the route, when illegal access or using system resource is occurred on networks. The logging system therefore, might be the first target of intruder. We developed the logging system which writes the information of logging and command execution on UNIX system. And we prepared the self-protecting functions of blocking intruder's attack on the logging system. They are protecting the logging process and the log file. To protect the logging process, we made it keep changing the process ID to avoid the intruder's attack. To protect the log file, we use hard link and mandatory file locking, so it can make it impossible to delete or change log file.

• Korean Management Science Review
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• v.20 no.2
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• pp.1-16
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• 2003

Previous research on data extraction and integration for data warehousing has concentrated mainly on the relational DBMS or partly on the object-oriented DBMS. Mostly, it describes issues related with the change data (deltas) capture and the incremental update by using the triggering technique of active database systems. But, little attention has been paid to data extraction approaches from other types of source systems like hierarchical DBMS, etc. and from source systems without triggering capability. This paper argues, from the practical point of view, that we need to consider not only the types of information sources and capabilities of ETT tools but also other factors of source systems such as operational characteristics (i.e., whether they support DBMS log, user log or no log, timestamp), and DBMS characteristics (i.e., whether they have the triggering capability or not, etc), in order to find out appropriate data extraction techniques that could be applied to different source systems. Having applied several different data extraction techniques (e.g., DBMS log, user log, triggering, timestamp-based extraction, file comparison) to S bank's source systems (e.g., IMS, DB2, ORACLE, and SAM file), we discovered that data extraction techniques available in a commercial ETT tool do not completely support data extraction from the DBMS log of IMS system. For such IMS systems, a new date extraction technique is proposed which first creates Index database and then updates the data warehouse using the Index database. We illustrates this technique using an example application.

• The KIPS Transactions:PartA
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• v.14A no.1 s.105
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• pp.39-46
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• 2007

Recently flash memory is widely accepted as a storage devise of embedded systems for portability and performance reasons. Flash memory has many distinguishing features compared to legacy magnetic disks. Especially, a file system for flash memory usually assumes the form of log-structured file system and it employs garbage collector accordingly. Since the garbage collector can greatly affect the performance of file system, it should be designed carefully considering flash memory features. In this paper, we suggest a new garbage collector for existing JFFS2 (Journaling Flash File System II) file system. By extensive performance evaluation, we show that the proposed garbage collector achieves improved performance in terms of flash memory consumption rate, increased flash memory life time, and improved wear-leveling.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.963-967
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• 2009

The Log-structured File System (LFS) collects all modified data into a memory buffer and writes them sequentially to a segment on disk. Therefore, it has the potential to utilize the maximum bandwidth of storage devices where sequential writes are much faster than random writes. However, as disk space is finite, LFS has to conduct cleaning to produce free segments. This cleaning operation is the main reason LFS performance deteriorates when file system utilization is high. To overcome painful cleaning and reduced performance of LFS, we propose the segment space recycling (SSR) scheme that directly writes modified data to invalid areas of the segments and describe the classification method of data and segment to consider locality of reference for optimizing SSR scheme. We implement U-LFS, which employs our segment space recycling scheme in LFS, and experimental results show that SSR scheme increases performance of WOLF by up to 1.9 times in HDD and 1.6 times in SSD when file system utilization is high.

• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.85-92
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• 2019

In this paper, we extract the quantitative relation data of activities from the workflow event log file recorded in the XES standard format and connect them to rediscover the workflow process model. Extract the workflow process patterns and proportions with the rediscovered model. There are four types of control-flow elements that should be used to extract workflow process patterns and portions with log files: linear (sequential) routing, disjunctive (selective) routing, conjunctive (parallel) routing, and iterative routing patterns. In this paper, we focus on four of the factors, disjunctive routing, and conjunctive path. A framework implemented by the authors' research group extracts and arranges the activity data from the log and converts the iteration of duplicate relationships into a quantitative value. Also, for accurate analysis, a parallel process is recorded in the log file based on execution time, and algorithms for finding and eliminating information distortion are designed and implemented. With these refined data, we rediscover the workflow process model following the relationship between the activities. This series of experiments are conducted using the Large Bank Transaction Process Model provided by 4TU and visualizes the experiment process and results.