• KIISE Transactions on Computing Practices
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• v.24 no.3
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• pp.129-137
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• 2018

NVMe(Non-Volatile Memory Express) SSD(Solid State Drive) is a high-performance storage that makes use of flash memory as a storage cell, PCIe as an interface and NVMe as a protocol on the interface. It supports multiple I/O queues which makes it feasible to process parallel-I/Os on multi-core environments and to provide higher bandwidth than SATA SSDs. Hence, NVMe SSD is considered as a next generation-storage for data-center and cloud computing system. However, in the virtualization system, the performance of NVMe SSD is not fully utilized due to the bottleneck of the software I/O stack. Especially, when it uses I/O stack of the hypervisor or the host operating system like Xen and KVM, I/O performance degrades seriously due to doubled-I/O stack between host and virtual machine. In this paper, we propose a new I/O engine, called Direct-AIO (Direct-Asynchronous I/O) engine, that can access NVMe SSD directly for I/O performance improvements on QEMU emulator. We develop our proposed I/O engine and analyze I/O performance differences between the existed I/O engine and Direct-AIO engine.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.781-788
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• 2005

LDA (Linear Discriminant Analysis) is a data discrimination technique that seeks transformation to maximize the ratio of the between-class scatter and the within-class scatter While it has been successfully applied to several applications, it has two limitations, both concerning the underfitting problem. First, it fails to discriminate data with complex distributions since all data in each class are assumed to be distributed in the Gaussian manner; and second, it can lose class-wise information, since it produces only one transformation over the entire range of classes. We propose three extensions of LDA to overcome the above problems. The first extension overcomes the first problem by modeling the within-class scatter using a PCA mixture model that can represent more complex distribution. The second extension overcomes the second problem by taking different transformation for each class in order to provide class-wise features. The third extension combines these two modifications by representing each class in terms of the PCA mixture model and taking different transformation for each mixture component. It is shown that all our proposed extensions of LDA outperform LDA concerning classification errors for handwritten digit recognition and alphabet recognition.

• Journal of KIISE:Software and Applications
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• v.26 no.9
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• pp.1085-1094
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• 1999

이동 분산 환경에서는 무선 데이타 전송 기법을 통하여 사용자들에게 다양한 정보들을 전달하게 된다. 본 논문은 균등 무선 데이타 방송 환경에서, 빠른 시간에 방송데이타를 접근할 수 있는 방법에 대하여 기술한다. 이를 위하여 무선 방송 데이타의 스케쥴링 문제를 정의하고, 어떤 질의가 접근하는 데이타들의 응집 정도를 나타내는 `질의 거리(Query Distance: QD)'라는 측정 기준을 제시한다. 제안한 질의 거리를 사용하여 각 질의의 우선 순위에 따라 해당 질의가 접근하는 데이타 집합을 방송 스케쥴에 추가하면서 스케쥴을 구성하는 데이타 스케쥴링 기법을 제시한다. 데이타 집합의 스케쥴 구성 과정에서 우선 순위가 높은 질의의 질의 거리를 최소화하면서 낮은 우선 순위 질의들의 질의 거리를 줄이는 스케쥴 확장 규칙들을 사용한다. 예를 이용하여 제안하는 방법에 대하여 설명한 후, 실험을 통해 제안한 방법의 성능을 평가한다.Abstract In mobile distributed systems the data on the air can be accessed by a lot of clients. This paper describes the way clients access the broadcast data in short latency in uniform wireless broadcasting environment. We define the problem of wireless data scheduling and propose a measure, named Query Distance(QD), which represents the coherence degree of data set accessed by a query. By using the measure, we give a data scheduling method that constructs the broadcast schedule by appending each query's data set in greedy way. When constructing the schedule, we use schedule expansion rules that reduce the QD's of lower-frequency queries while minimizing the QD's of the higher-frequency ones. With the use of examples we illustrate the mechanism of the proposed method and we test the performance of our method.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.156-166
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• 2002

Decision tree induction is a kind of useful machine learning approach for extracting classification rules from a set of feature-based examples. According to the partitioning style of the feature space, decision trees are categorized into univariate decision trees and multivariate decision trees. Due to observation error, uncertainty, subjective judgment, and so on, real-world data are prone to contain some errors in their feature values. For the purpose of making decision trees robust against such errors, there have been various trials to incorporate fuzzy techniques into decision tree construction. Several researches hove been done on incorporating fuzzy techniques into univariate decision trees. However, for multivariate decision trees, few research has been done in the line of such study. This paper proposes a fuzzy decision tree induction method that builds fuzzy multivariate decision trees named fuzzy oblique decision trees, To show the effectiveness of the proposed method, it also presents some experimental results.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.610-617
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• 2003

Similarity matching in video database is of growing importance in many new applications such as video clustering and digital video libraries. In order to provide efficient access to relevant data in large databases, there have been many research efforts in video indexing with diverse spatial and temporal features. however, most of the previous works relied on sequential matching methods or memory-based inverted file techniques, thus making them unsuitable for a large volume of video databases. In order to resolve this problem, this paper proposes an effective and scalable indexing technique using a trie, originally proposed for string matching, as an index structure. For building an index, we convert each frame into a symbol sequence using a window order heuristic and build a disk-resident trie from a set of symbol sequences. For query processing, we perform a depth-first search on the trie and execute a temporal segmentation. To verify the superiority of our approach, we perform several experiments with real and synthetic data sets. The results reveal that our approach consistently outperforms the sequential scan method, and the performance gain is maintained even with a large volume of video databases.

• Journal of KIISE:Databases
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• v.37 no.2
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• pp.113-120
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• 2010

XML is broadly using for data storing and processing. XML is specified its structural characteristic and user can query with XPath when information from data document is needed. XPath query can process when the tern and structure of document and query is matched with each other. However, nowadays there are lots of data documents which are made by using different terminology and structure therefore user can not know the exact idea of target data. In fact, there are many possibilities that target data document has information which user is find or a similar ones. Accordingly user query should be processed when their term usage or structural characteristic is slightly different with data document. In order to do that we suggest a XML document ranking method based on path similarity. The method can measure a semantic similarity between user query and data document using three steps which are position, node and relaxation factors.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.457-461
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• 2010

Even though face recognition researches have been developed for a long ago, there is no practical face recognition system in real life. It is caused by several real situations where non-facial components such as glasses, scarf, and hair occlude facial components while facial images in a face database are well designed. This occlusion decreases recognition performance. Previous approaches in recent years have tried to solve non-facial components but have not resulted in enough performance. In this paper, we propose a method to handle this problem based on support vector data description, which trains the hyperball in feature space to find the minimum distance estimating the approximated face. In order to evaluate its performance and validate the effectiveness of the proposed method, we make several experiments and the results show that the proposed method has a considerable effectiveness.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.3
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• pp.145-153
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• 2009

Trajectory data are ubiquitous in the real world. Recent progress on satellite, sensor, RFID, video, and wireless technologies has made it possible to systematically track object movements and collect huge amounts of trajectory data. Accordingly, there is an ever-increasing interest in performing data analysis over trajectory data. In this paper, we develop a data mining tool for massive trajectory data. This mining tool supports three operations, clustering, classification, and outlier detection, which are the most widely used ones. Trajectory clustering discovers common movement patterns, trajectory classification predicts the class labels of moving objects based on their trajectories, and trajectory outlier detection finds trajectories that are grossly different from or inconsistent with the remaining set of trajectories. The primary advantage of the mining tool is to take advantage of the information of partial trajectories in the process of data mining. The effectiveness of the mining tool is shown using various real trajectory data sets. We believe that we have provided practical software for trajectory data mining which can be used in many real applications.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.847-855
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• 2014

In recent years, women's monetary rewards in golf increased and their performances have improved significantly compared to other sports. Sports marketing has become more active in Asia and the number of Korean players in LPGA with good scores are increasing. For these reasons, golf is becoming increasingly popular. The prize money is higher than in other sports and the economic benefits are increasing due to the financial incentives such as sponsorships. Many of these prospects actively affect women's golf. Certain rookies continue to increase and their performances improve day by day. In this study, I analyze the changes in performance over time of last 5 years from 2009 using growth curve analysis. According to the results of analysis, driving distance and average putting skills developed but green in regulation decreased.

• The Journal of Korean Association of Computer Education
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• v.11 no.4
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• pp.71-83
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• 2008

The recent advances of the information technology have motivated lots of research efforts to develop new computer-aided teaching and learning methodologies on various computer science topics, such as data structures, operating system, computer networks, and computer architecture. However, there have been only few studies to educate the database subject although it is one of the most important topics in the computer science. Specifically, among the various issues in the database subject, a learner often suffers to understand the mechanism of the concurrency control and recovery of database transaction in the database because it highly interacts with other functions in the database. Obviously, an intelligent visualization tool can help a learner to understand the process of the concurrency control and the recovery of database transaction. The purpose of this study is to develop an efficient visualization tool which can help users understand the two phase locking protocol (2PLP)-based concurrency control. Specifically, this visualization tool is designed to encourage a users' participation and raise their interest by visualizing the process of transactions and allowing users to specify and manipulate their own transactions.