• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.312-317
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• 2005

Ubiquitous computing has had an effect to politics, economics, society, culture, education and so forth. For effective management of huge Ubiquitous networks environment, various computers which are connected to networks has to decide automatic optimum with intelligence. Currently in many areas, data mining has been used effectively to construct intelligent systems. We proposed a hybrid support vector machine for Ubiquitous data mining which realized intelligent Ubiquitous computing environment. Many data were collected by sensor networks in Ubiquitous computing environment. There are many noises in these data. The aim of proposed method was to eliminate noises from stream data according to sensor networks. In experiment, we verified the performance of our proposed method by simulation data for Ubiquitous sensor networks.

• The Transactions of the Korea Information Processing Society
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• v.5 no.4
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• pp.924-941
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• 1998

It is not easy to access to the required data from the Web by using search engines because there are too many data selected and they do not provide enough information related to the corresponding data. Metadata is data about data. It includes information about data itself and contents of data as well. Users can acquire enough information about the corresponding data and access to the required data exactly using metadata, and therefore the data usability will be increased. In this paper, several metadata technologies and metadata models that are already in process of standardization or adopted as standards are analyzed, and the SeriCore Metadata Model for documents such as papers, project reports, technical reports, abstracts, and manuals, and graphic images that are in the field of science technologies on the Web is proposed. The SeriCore Metadata Management System that can generate, store, and retrieve metadata effectively is designed and implemented based on the SeriCore Metadata Model.

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.117-124
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• 2001

A data mining system includes various data mining functions such as aggregation, association and classification, among others. To express these data mining function, a powerful data mining query language is needed. In addition, a graphic user interface(GUI) based on the data mining query language is needed for users. In addition, processing a data mining query targeted for a data warehouse, which is the appropriate data repository for decision making, is needed. In this paper, we first build a GUI to enable users to easily define data mining queries. We then propose a data mining query processing framework that can be used to process a data mining query targeted for a data warehouse. We also implement a schema generate a data warehouse schema that is needed to build a data warehouse. Lastly, we show the implementation details of a query processor that can process queries that discover association rules.

• The Journal of Bigdata
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• v.5 no.1
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• pp.77-88
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• 2020

Recently, many researches habe been done to organize and analyze various complex relationships in real world, represented in the form of graphs. In particular, the computer field literature data system, such as DBLP, is a representative graph data in which can be composed of papers, their authors, and citation among papers. Becasue graph data is very complex in storage structure and expression, it is very difficult task to search, analysis, and visualize a large size of bibliographic big data. In this paper, we develop a graphic user interface tool, called EEUM, which visualizes bibliographic big data in the form of graphs. EEUM provides the features to browse bibliographic big data according to the connected graph structure by visually displaying graph data, and implements search, management and analysis of the bibliographc big data. It also shows that EEUM can be conveniently used to search, explore, and analyze by applying EEUM to the bibliographic graph big data provided by DBLP. Through EEUM, you can easily find influential authors or papers in every research fields, and conveniently use it as a search and analysis tool for complex bibliographc big data, such as giving you a glimpse of all the relationships between several authors and papers.

• The KIPS Transactions:PartD
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• v.12D no.2 s.98
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• pp.219-232
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• 2005

With the rapid progress of computer technology in recent years, a digital video data has been used in many applications. As a result, various technologies have been introduced to discover knowledge embedded in a video database. However, few researches on data mining for a video database have been performed due to the unclear boundary of the information in a large amount of a video stream. This paper proposes an efficient generation method of a feature profile in a set of similar video data. To extract the information embedded in original video data efficiently, several refinement techniques are also presented. These methods include merging pixels, restricting preferred areas, removing noises under a minimum repeat factor, extracting important key frames, generating derived blocks and applying weights to dynamic and static areas differently. Finally, the performance of these methods is evaluated by comparing a result profile obtained by a data mining process with original video streams.

• Journal of Korea Spatial Information System Society
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• v.8 no.1 s.16
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• pp.77-92
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• 2006

This paper proposes an efficient spatial data manager for map services in mobile environment. It is designed to provide interoperability and efficient performance at once. To provide interoperability and reusability, the spatial data manager conforms to international standards such as the OpenGIS Simple Features Implementation Specification for OLE/COM, OpenGIS Geography Markup Language (GML) Encoding Specification developed by the Open Geospatial Consortium (OGC). The spatial data manger exploits physical main memory using Address Windowing Extensions supported by Microsoft Windows to manage spatial data for efficient performance by reducing time to read data from disk on user's request. The format of the spatial data in main memory is target data (GML) to reduce conversion time from source data to it. Progressive transmission is also provided to reduce latency time by representing only received partial data for mobile environment without waiting whole transmission.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.13-19
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• 2019

The accuracy of machine learning is greatly affected by amount of learning data and quality of data. Collecting existing Web-based learning data has danger that data unrelated to actual learning can be collected, and it is impossible to secure data transparency. In this paper, we propose a method for collecting data directly in parallel by blocks in a block - chain structure, and comparing the data collected by each block with data in other blocks to select only good data. In the proposed system, each block shares data with each other through a chain of blocks, utilizes the All-reduce structure of Parallel-SGD to select only good quality data through comparison with other block data to construct a learning data set. Also, in order to verify the performance of the proposed architecture, we verify that the original image is only good data among the modulated images using the existing benchmark data set.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.81-84
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• 2006

유비쿼터스 컴퓨팅 환경으로 발전하면서 문자열 위주의 획일적 형태에서 음성, 이미지 등 다양한 형태의 데이터들을 처리하게 되었으며, 또한 빠르고 정확하게 처리되기를 요구하고 있다. 현재 데이터 처리 중심부에 있는 Database는 대부분이 Relation DB 위주로 되어 있어 Datafile 에 데이터를 저장하고 있어 대용량의 이미지 데이터 처리에 적합하지가 않다. 본 논문에서는 이러한 단점을 보강하기 위해 Relation DB 하에서 대용량의 이미지 데이터 처리를 가능하게 하는 기법을 제시한다. 이렇게 함으로써 이미지 데이터를 Upload, Download 시 따른 응답 속도를 보장 할 수 있도록 LRU 알고리즘 기반으로 제안을 하였다. 본 논문에서 제안된 기법은 시뮬레이션을 통해 (1)기존 RDB(Relational Database)의 BLOB(Binary Large Object)필드를 이용한 이미지 데이터 처리 방식, (2)별도의 저장 공간에 이미지 데이터를 입/출하는 방식, (3)별도의 저장 공간에 이미지 데이터를 입/출력할 때 LRU(least Recently Used)알고리즘을 이용하는 방식에 대하여 성능 평가를 하였다. 그 결과 (3)별도의 저장 공간에 LRU(least Recently Used)알고리즘을 이용하여 입/출력하는 방식이 (1)기존의 RDB(Relational Database)형태에 BLOB(binary large object)필드를 이용한 것 보다 성능이 높음을 확인하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2001.11b
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• pp.39-42
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• 2001

본 논문은 현재 다각적으로 논의되는 디지털 위성방송 및 데이터 방송 서비스를 제공하기 위해 랜 기반의 IP 망과 DVB 에서 규정한 위성망을 연결시켜 줌으로써 위성을 통한 다양한 멀티캐스트 및 유니캐스트 서비스를 제공할 수 있도륵 해주는 데이터 인코더의 하드웨어/소프트웨어 설계 및 구현에 관하여 기술한다. 다양한 위성 방송 서비스를 제공하기 위해서 데이터 인코더는 IP 데이터를 이더넷을 통해 입력받아 DVB 데이터 방송[2] 규격에서 규정한 MPE 포맷으로 켑슐화 한 후 MPEG-2 TS 패킷으로 변환하여 DVB-ASI 신호로 바꾸어 다중화기로 출력한다. 아울러 사용자 단말에서 서비스 수신을 위해 필요한 PSI/SI 정보와 서비스 형태에 따라 부가적으로 발생하는 사설 데이터를 처리해야 한다. 본 논문에서 기술하는 데이터 인코더는 4096개의 PID 할당이 가능하며 20M0ps 이상의 데이터 처리가 가능하여 동시에 여러 개의 서비스 제공이 가능하다.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.867-870
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• 2003

본 논문은 멀티미디어 데이터 처리를 위한 효율적인 RISC 프로세서 유닛의 설계를 목표로 Vector 프로세서의 SIMD(Single Instruction Multiple Data) 개념을 바탕으로 고정된 연산기 데이터 비트 수에 비해 상대적으로 작은 비트수의 데이터 연산의 부분 병렬화를 통하여 멀티미디어 데이터 연산의 기본이 되는 곱셈누적(MAC : Multiply and Accumulate) 연산의 성능을 향상 시킨다. 또한 기존의 MMX나 VIS 등과 같은 범용 프로세서들의 부분 병렬화를 위해 전 처리 과정의 필요충분조건인 데이터의 연속성을 위해 서로 다른 길이의 데이터 흑은 비트 수가 작은 멀티미디어의 데이터를 하나의 데이터로 재처리 하는 재정렬 혹은 Packing/Unpacking 과정이 성능 전체적인 성능 저하에 작용하게 되므로 본 논문에서는 기존의 프로세서의 연산기 구조를 재이용하여 병렬 곱셈을 위한 연산기 구조를 구현하고 이를 위한 데이터 정렬 연산 구조를 제안한다.