• Journal of Korea Multimedia Society
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• v.8 no.2
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• pp.154-163
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• 2005

Because most researches that were studied so far on XML documents used an absolute coordinate system in most of the index techniques, the update operation makes a large burden. To express the structural relations between elements, attributes and text, we need to reconstruct the structure of the coordinates. As the reconstruction process proceeds through out the entire XML document in a cascade manner, which is not limited to the current changing node, a serious performance problem may be caused by the frequent update operations. In this paper, we propose an index technique based on extensible index that does not cause serious performance degradations. It can limit the number of node to participate in reconstruction process and improve lots of performance capacities on the whole. And extensible index performs the containment relationship query by the simple expression using SQL statement.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.22-30
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• 2023

The portable mobile devices with high performance and high speed 5G network activate and explode the demands for ubiquitous information services that remove the limitations of time for the communication and places to request for the information. NN (Nearest Neighbor) search is one of the most important types of queries to be processed efficiently in the information services. Various indexes have been proposed to support efficient NN search in the wireless broadcast system. The indexes adopting Hilbert curve, grid partition or Voronoi diagram enable the clients to search for NN quickly in the wireless broadcast channel. It is necessary that an efficient means to evaluate the performances of various indexes. In this paper, we propose an open framework that can adopt a variety of indexing schemes and evaluate and compare the performances of them. The proposed framework is organized with open and flexible structure that can adopt hybrid indexing schemes extensible to Voronoi diagram as well as simple indexing schemes. With the implemented framework, we demonstrate the efficiency and scalability and flexibility of the proposed framework by evaluating various indexing schemes for NN query.

• Journal of the Korea Computer Industry Society
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• v.5 no.1
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• pp.87-100
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• 2004

XML has recently considered as a new standard for data presentation and exchange on the web, many researches are on going to develop applications and index mechanism to store and retrieve XML documents efficiently. In this paper, design a Main-Memory based XML storage system for efficient management of XML document. And propose structured retrieval of XML document tree which reduce the traverse of XML document tree using element type information included user queries. Proposed indexing mechanism has flexibilities for dynamic data update. Finally, for query processing of XML document include Link information, design a index structure of table type link information on observing XLink standards.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.27-30
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• 2005

XML(Extensible Markup Language)이 데이터 표현(data representation)과 문서 교환(data exchange)의 표준으로 지정됨에 따라 데이터베이스(database, DB)에 XML 문서를 저장하고 질의하기 위한 연구가 활발히 진행되고 있다. 특히, 현재 주류를 이루고 있는 관계형 DB 에 저장하기 위한 XML 인덱싱(indexing) 기법에 대한 연구도 다양하게 진행되고 있다. 본 논문에서는 XML 문서를 관계형 DB 에 효율적으로 저장하고 질의하기 위한 방법으로서 기존의 트리(tree) 구조의 데이터를 관계형 DB 에 Nested Interval 인덱싱 기법을 적용하여 XML 문서를 저장하는 방법에 대해 연구한다. 기존의 저장 기법들의 경우 XML 문서를 효율적으로 질의하기 위한 인덱싱을 수행하기 때문에 입력 후 추가되는 노드(node), 혹은 노드 집합의 입력 시에는 전체 혹은 일부분의 XML 문서를 재-인덱싱 해야 하는 비효율이 있다. 그러나, Nested Interval 의 경우에는 재-인덱싱이 불필요하다. 본 논문에서는 기존의 트리 구조 데이터의 인덱싱 기법들에 대한 비교와 함께 Nested Interval 을 이용한 XML 문서의 인덱싱 기법에 대해 기술한다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.