• 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.

• Convergence Security Journal
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• v.7 no.3
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• pp.95-100
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• 2007

Alternate Data Streams (ADS) in NTFS originally has developed to provide compatibility with Macintosh Hierarchical File System. However, it is being used by the malware writers in order to support hiding malwares or data for the purpose of anti-forensics. Therefore identifying if hidden ADSs exist and extracting them became one of the most important component in computer forensics. This paper proposes a method to detect ADSs using MFT information. Experiment reveals that proposed method is better in performance and detection rate then others. This method supports not only identification of ADSs which are being used by the operating systems but also investigation of both live systems and evidence images. Therefore it is appropriate for using forensic purpose.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.189-198
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• 2009

Unstructured peer-to-peer systems are most commonly used in today's internet. But file placement is random in these systems and no correlation exists between peers and their contents. There is no guarantee that flooding queries will find the desired data. In this paper, we propose to cluster nodes in unstructured P2P systems using the agglomerative hierarchical clustering algorithm to improve the search method. We compared the delay time of clustering the nodes between our proposed algorithm and the k-means clustering algorithm. We also simulated the delay time of locating data in a network topology and recorded the overhead of the system using our proposed algorithm, k-means clustering, and without clustering. Simulation results show that the delay time of our proposed algorithm is shorter compared to other methods and resource overhead is also reduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.859-876
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• 2010

In this paper, we propose a hierarchical application traffic classification system as an alternative means to overcome the limitations of the port number and payload based methodologies, which are traditionally considered traffic classification methods. The proposed system is a new classification model that hierarchically combines a binary classifier SVM and Support Vector Data Descriptions (SVDDs). The proposed system selects an optimal attribute subset from the bi-directional traffic flows generated by our traffic analysis system (KU-MON) that enables real-time collection and analysis of campus traffic. The system is composed of three layers: The first layer is a binary classifier SVM that performs rapid classification between P2P and non-P2P traffic. The second layer classifies P2P traffic into file-sharing, messenger and TV, based on three SVDDs. The third layer performs specialized classification of all individual application traffic types. Since the proposed system enables both coarse- and fine-grained classification, it can guarantee efficient resource management, such as a stable network environment, seamless bandwidth guarantee and appropriate QoS. Moreover, even when a new application emerges, it can be easily adapted for incremental updating and scaling. Only additional training for the new part of the application traffic is needed instead of retraining the entire system. The performance of the proposed system is validated via experiments which confirm that its recall and precision measures are satisfactory.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10a
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• pp.547-551
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• 2006

디렉터리와 파일의 계층적 구조를 가지는 계층적 파일 시스템은 오늘날 대부분의 범용 컴퓨터에서 흔히 사용되고 있다. 계층적 파일 시스템은 직관적이고, 체계적이며, 단순하다는 장점이 있으나 검색이 용이 하지 않으며, 메타데이터를 관리하기 어렵다는 단점이 존재한다. 본 연구에서는 계층적 파일 시스템의 장점과 빠른 검색기능을 활용하여 메타데이터를 검색하고 관리할 수 있는 데이터베이스의 장점을 결합하여 계층적 파일 시스템에서 메타데이터를 관리할 수 있는 방법을 제안하였다. 데이터 그리드와 같이 분산된 데이터 저장 장치를 연동하여야 하는 경우에 원격지에 있는 파일 시스템의 파일들을 검색하는 일이 빈번히 수행되는데, 이 경우 본 연구에서 제안한 방법을 사용하면 효과적인 시스템을 기대할 수 있다.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.25-32
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• 2013

Purpose: The objective of this study is to develop a data middleware for u-IT convergence in agricultural environment monitoring, which can support non-standard data interfaces and solve the compatibility problems of heterogenous sensor networks. Methods: Six factors with three different interfaces were chosen as target data among the environmental monitoring factors for crop cultivation. PostgresSQL and PostGIS were used for database and the data middleware was implemented by Python programming language. Based on hierarchical model design and key-value type table design, the data middleware was developed. For evaluation, 2,000 records of each data access interface were prepared. Results: Their execution times of File I/O interface, SQL interface and HTTP interface were 0.00951 s/record, 0.01967 s/record and 0.0401 s/record respectively. And there was no data loss. Conclusions: The data middleware integrated three heterogenous sensor networks with different data access interfaces.

• Spatial Information Research
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• v.19 no.3
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• pp.43-52
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• 2011

Recently, interest in location-based service (LBS) which utilizes a DBMS in mobile system environment has been increasing, and it is expected to overcome the existing file-based system's limitation in advanced in-vehicle system by utilizing DBMS's advantages such as efficient storage, transaction management, modelling and spatial queries etc. In particular, the road network data corresponds to the most essential domain in a route planning system, which needs efficient management and maintenance. Accordingly, this study aims to develop an efficient graph-based geodata model for topological network data and to support dynamic path computation algorithm based on heuristic approach in mobile LBS system. To achieve this goal, we design a data model for supporting the hierarchy of network, and implement a path planning system to evaluate its performance in mobile LBS system. Last but not least, we find out that the designed path computation algorithm with hierarchical graph model reduced the number of nodes used for finding and improved the efficiency of memory.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.7-14
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• 2010

In this paper, we introduce a hierarchical internet application traffic classification system based on SVM as an alternative overcoming the uppermost limit of the conventional methodology which is using the port number or payload information. After selecting an optimal attribute subset of the bidirectional traffic flow data collected from the campus, the proposed system classifies the internet application traffic hierarchically. The system is composed of three layers: the first layer quickly determines P2P traffic and non-P2P traffic using a SVM, the second layer classifies P2P traffics into file-sharing, messenger, and TV, based on three SVDDs. The third layer makes specific classification of the entire 16 application traffics. By classifying the internet application traffic finely or coarsely, the proposed system can guarantee an efficient system resource management, a stable network environment, a seamless bandwidth, and an appropriate QoS. Also, even a new application traffic is added, it is possible to have a system incremental updating and scalability by training only a new SVDD without retraining the whole system. We validate the performance of our approach with computer experiments.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.21-31
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• 2007

The intellectual property system was very important to the past industrial society. It is so important to the 21C information age. It is a leading role to developing these information society. Not only the digital content control but the technology of software source code for the intellectual property is so much mean to international competition. On occurring disputation property, we have to prove the fact, there is a problem to discriminate the original source code. In this paper, we make a study of the digital licence prototype for discriminate the original source code. Reserved words of software source code by parsing express to XML file that have hierarchical structure. Then, we can express architecture of software source code by tree structure form instead of complex source code. And we make a study of the indexing and searching to search digital license.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.1027-1038
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• 2006

There are many technical problems in the recommendation system based on very large database(VLDB). So, it is necessary to study the recommendation system' structure and the data-mining technique suitable for the large scale Internet shopping mail. Thus we design and implement the product recommendation system using k-means clustering algorithm and sequential pattern technique which can be used in large scale Internet shopping mall. This paper processes user information by batch processing, defines the various categories by hierarchical structure, and uses a sequential pattern mining technique for the search engine. For predictive modeling and experiment, we use the real data(user's interest and preference of given category) extracted from log file of the major Internet shopping mall in Korea during 30 days. And we define PRP(Predictive Recommend Precision), PRR(Predictive Recommend Recall), and PF1(Predictive Factor One-measure) for evaluation. In the result of experiments, the best recommendation time and the best learning time of our system are much as O(N) and the values of measures are very excellent.