• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.4
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• pp.393-409
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• 2003

In this paper, we present a multi-task debugging environment for Qplus-T embedded-system such as internet information appliances. We will propose the structure and functions of a remote multi-task debugging environment supporting environment effective ross-development. And, we are going enhance the communication architecture between the host and target system to provide more efficient cross-development environment. The remote development toolset called Q+Esto consists to several independent support tools: an interactive shell, a remote debugger, a resource monitor, a target manager and a debug agent. Excepting a debug agent, all these support tools reside on the host systems. Using the remote multi-task debugger on the host, the developer can spawn and debug tasks on the target run-time system. It can also be attached to already-running tasks spawned from the application or from interactive shell. Application code can be viewed as C/C++ source, or as assembly-level code. It incorporates a variety of display windows for source, registers, local/global variables, stack frame, memory, event traces and so on. The target manager implements common functions that are shared by Q+Esto tools, e.g., the host-target communication, object file loading, and management of target-resident host tool´s memory pool and target system´s symbol-table, and so on. These functions are called OPEn C APIs and they greatly improve the extensibility of the Q+Esto Toolset. The Q+Esto target manager is responsible for communicating between host and target system. Also, there exist a counterpart on the target system communicating with the host target manager, which is called debug agent. Debug agent is a daemon task on real-time operating systems in the target system. It gets debugging requests from the host tools including debugger via target manager, interprets the requests, executes them and sends the results to the host.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.199-208
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• 2019

As the number of available satellites increases and technology advances, image information outputs are becoming increasingly diverse and a large amount of data is accumulating. In this study, we propose a change detection method for high-resolution satellite images that uses transfer learning and a deep learning network to overcome the limit caused by insufficient training data via the use of pre-trained information. The deep learning network used in this study comprises convolutional layers to extract the spatial and spectral information and convolutional long-short term memory layers to analyze the time series information. To use the learned information, the two initial convolutional layers of the change detection network are designed to use learned values from 40,000 patches of the ISPRS (International Society for Photogrammertry and Remote Sensing) dataset as initial values. In addition, 2D (2-Dimensional) and 3D (3-dimensional) kernels were used to find the optimized structure for the high-resolution satellite images. The experimental results for the KOMPSAT-3A (KOrean Multi-Purpose SATllite-3A) satellite images show that this change detection method can effectively extract changed/unchanged pixels but is less sensitive to changes due to shadow and relief displacements. In addition, the change detection accuracy of two sites was improved by using 3D kernels. This is because a 3D kernel can consider not only the spatial information but also the spectral information. This study indicates that we can effectively detect changes in high-resolution satellite images using the constructed image information and deep learning network. In future work, a pre-trained change detection network will be applied to newly obtained images to extend the scope of the application.

• Journal of Internet Computing and Services
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• v.6 no.6
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• pp.13-22
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• 2005

This paper aims to develop a system for converting web contents to mobile contents that can be used on mobile devices. Since web contents generally consist of pop-up ad windows, a bunch of unnecessary images and useless links, it is difficult to efficiently display them on common mobile devices that have lower bandwidth and memory, as well as much smaller screen, than the online environment. It is also troublesome for mobile device users to directly access contents. Thus, there has been a great demand for a new method for extracting useful and adequate contents from web documents, and optimizing them for use on mobile phones, In the paper, a system based on WAP 2,0 and XHTML Basic, which is a content creation language adopted for WAP 2,0, has been suggested. The system is designed to convert web contents by using the conversion rules of the existing filtering method after making the size of web documents smaller. The adopted conversion rules use the XHTML Basic's module units so that modification and deletion can be carried out with ease. In addition, it has been defined in a XSL document written in XSLT to maintain the extensibility of conversion and the validity of documents, In order to allow it to efficiently work together with WAP l.X's legacy services, the system has been built in a way that can have modules, which analyze information about CC/PP profiles and mobile device headers.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.3
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• pp.240-247
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• 2001

Java is a promising runtime environment for embedded systems because it has many advantages such as platform independence, high security and support for multi-threading. One of the most famous Java run-time environments, Sun's ($PersonalJave^{TM}$) is based on Truffle architecture, which enables programmers to design various GUIs easily. For this reason, it has been ported to various embedded systems such as set-top boxes and personal digital assistants(PDA's). Basically, Truffle uses heavy-weight window managers such as Microsoft vVin32 API and X-Window. However, those window managers are not adequate for embedded systems because they require a large amount of memory and disk space. To come up with the requirements of embedded systems, we adopt Microwindows as the platform graphic system for Personal] ava A WT onto Embedded Linux. Although Microwindows is a light-weight window manager, it provides as powerful API as traditional window managers. Because Microwindows does not require any support from other graphics systems, it can be easily ported to various platforms. In addition, it is an open source code software. Therefore, we can easily modify and extend it as needed. In this paper, we implement Personal]ava A WT using Microwindows on embedded Linux and prove the efficiency of our approach.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.169-169
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• 2017

스마트 돈사 내의 열환경 분석에 필수적으로 고려되어야 인자는 가축의 복사 에너지 변화로 볼 수 있다. 열환경 제어의 대상이기도 하지만 회귀적으로 열환경 변화의 인자이기도 하다. 이러한 가축의 복사 에너지 분석을 위하여 시설 내에 용이하게 배포가 가능한 열화상 계측 시스템을 개발하였다. 초소형 마이크로 열화상 계측 시스템에 부가적으로 IOT(Internet of Thing) 기반 기술을 이용한 모듈화 개발을 병행하였다. 열화상 계측 센서로 LWIR(Longwave infrared)영역에 해당하는 $8{\mu}m{\sim}4{\mu}m$의 영역에서 $0.05^{\circ}C$의 분해능을 보이는 $Lepton^{TM}$ (500-0690-00, FLIR, Goleta, CA)모델을 사용하였다. SPI(Serial Peripheral Interface) 속도 2 Mhz로 마이크로프로세서(NanoPi NEO Air, FrendlyArm, CA, USA)와 고속 통신을 수행하여 9 Hz의 계측이 가능하다. 열화상 센서와 마이컴으로 구성되는 단위 계측 시스템의 통신 기능 확장을 위하여 다음과 같이 세 단계의 정보 전달 시나리오를 설계하였다. 1) 단독적으로 열화상을 계측 하고 내장된 메모리에 저장하는 형식 2) 인접한 사용자 인터페이스에서 1번 단독 모듈에 접속하여 열화상을 실시간으로 전송하여 화면에 도시하는 형식 3) 2번 사용자 도시모듈과 병행적으로 Local WI-FI 통신을 이용한 모바일 기기에 화면을 도시하는 형식. 이와 같은 계층적이며 모듈화된 계측 시스템을 구성하기 위해서 1번 모듈에 공개 소프트웨어인 Hostapd 2.5(http://w1.fi/hostapd)버전을 설치하였다. 외부 인터넷 환경이 없는 상황에 1번 모듈 단독으로 AP(Access Point) 기능을 제공하여 지근 거리에 있는 2번 모듈과 3번 모바일 기기의 접속을 관리할 수 있다. 2번 모듈의 경우 화면 다수의 1번 모듈에 접속을 교차적으로 수행하는 방식과 2번 모듈 자체가 AP가 되어 1번 모듈의 접속을 허용하는 형태로 구성되어 있다. 계측 시스템의 계측 매트릭스 구성에 따라 선택적으로 결정할 수 있다. 1번 2번 모듈 공통적으로 TCP/IP Listener와 Client 서비스를 병렬적으로 수행할 수 있도록 개발을 하였다. 3번 모바일 기기에서 사용자 인터페이스 구현을 위하여 범용 Android 기반 GUI 프로그램과 Socket 통신을 연동시켰다. 1개의 열화상 Frame의 전송량은 9,600 Byte ($=80{\times}60{\times}2Byte$) 로 WI-FI 통신 전송 시 2회 ~ 6회 정도 내외로 가변적인 통신 수행 횟수를 나타내었다. 센서 계측 시스템과 정보 전송 시스템을 병렬적으로 구성한 모듈화 된 계측시스템의 전 요소에서 센서에서 제공하는 최대 계측 주기인 9 Hz 구현이 일반적으로 가능하였다. 이를 이용한 추후 연구를 통해 가축 객체의 열복사 정보와 돈사 내 열환경 간의 역학성을 연구할 것이다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1465-1475
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• 2015

Cheating detection models in the online games can be divided into two parts. The one is on client based model, which is designed to detect malicious programs not to be run while playing the games. The other one is server based model, which distinguishes the difference between benign users and cheaters by the server log analysis. The client based model provides various features to prevent games from cheating, For instance, Anti-reversing, memory manipulation and so on. However, being deployed and operated on the client side is a huge weak point as cheaters can analyze and bypass the detection features. That Is why the server based model is an emerging way to detect cheating users in online games. But the simple log data such as FPS's one can be hard to find validate difference between two of them. In this paper, In order to compensate for the disadvantages of the two detection model above, We use the existing game security solution log as well as the server one to bring high performance as well as detection ratio compared to the existing detection models in the market.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.1
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• pp.99-113
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• 2004

A new architecture called SAN(Storage Area Network) was developed in response to the requirements of high availability of data, scalable growth, and system performance. In order to use SAN more efficiently, most of SAN operating softwares support storage virtualization concepts that allow users to view physical storage devices attached to SAN as a large volume virtually h logical volume manager plays a key role in storage virtualization. It realizes the storage virtualization by mapping logical addresses to physical addresses. A logical volume manager also supports a snapshot that preserves a volume image at certain time and on-line reorganization to allow users to add/remove storage devices to/from SAN even while the system is running. To support the snapshot and the on-line reorganization, most logical volume managers have used table based mapping methods. However, it is very difficult to manage mapping table because the mapping table is large in proportion to a storage capacity. In this paper, we design and implement an efficient and flexible hybrid mapping method based on mathematical equations. The mapping method in this paper supports a snapshot and on-line reorganization. The proposed snapshot and on-line reorganization are performed on the reserved area which is separated from data area of a volume. Due to this strategy normal I/O operations are not affected by snapshot and reorganization. Finally, we show the superiority of our proposed mapping method through various experiments.

• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.774-779
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• 2015

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner using Apache Spark, an in-memory high speed cluster computing environment, which is effective for sequencing and iterating component reasoning jobs. The proposed reasoner can not only check the integrity of a large-scale spatial knowledge base representing topological and directional relationships between spatial objects, but also expand the given knowledge base by deriving new facts in highly efficient ways. In general, qualitative reasoning on topological and directional relationships between spatial objects includes a number of composition operations on every possible pair of disjunctive relations. The proposed reasoner enhances computational efficiency by determining the minimal set of disjunctive relations for spatial reasoning and then reducing the size of the composition table to include only that set. Additionally, in order to improve performance, the proposed reasoner is designed to minimize disk I/Os during distributed reasoning jobs, which are performed on a Hadoop cluster system. In experiments with both artificial and real spatial knowledge bases, the proposed Spark-based spatial reasoner showed higher performance than the existing MapReduce-based one.