• Journal of KIISE:Databases
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• v.31 no.5
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• pp.553-566
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• 2004

Some of the current main research areas involving techniques related to XML consist of storing XML documents, optimizing the query, and indexing. As such we may focus on the set of documents that are composed of various structures, but that are not shared with common structure such as the same DTD or XML Schema. In the case, it is essential to analyze structural similarities and differences among many documents. For example, when the documents from the Web or EDMS (Electronic Document Management System) are required to be merged or classified, it is very important to find the common structure for the process of handling documents. In this paper, we transformed sequential pattern mining algorithms(1) to extract maximal similar paths between two XML documents. Experiments with XML documents show that our transformed sequential pattern mining algorithms can exactly find common structures and maximal similar paths between them. For analyzing experimental results, similarity metrics based on maximal similar paths can exactly classify the types of XML documents.

• Journal of the Korea Society of Computer and Information
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• v.20 no.3
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• pp.9-17
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• 2015

The ISO 26262 standard requires a preliminary hazard analysis and risk assesment early in the development for automotive system. This is a first step for the development of an automotive system to determine the necessary safety measures to be implemented for a certain function. In this paper, we propose an ontology-based hazard analysis and risk assessment method for automotive functional safety. We use ontology to model the hazard and SWRL(Semantic Web Language) to describe risk analysis. The applicability of the proposed method is evaluated by the case study of an ESCL(electronic steering column lock) system. The result show that ontology deduction is useful for improving consistency and accuracy of hazard analysis and risk assessment.

• Journal of Korea Spatial Information System Society
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• v.7 no.1 s.13
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• pp.39-52
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• 2005

GML (Geography Markup Language) has become the widely adopted standard for transport and storage of geographic information. So, various researches such as modeling, storage, query, and etc have been studied to provide the interoperability of geographic information in web environments. Especially, there are increased needs to store semi-structured data such as GML documents efficiently. Therefore, in this paper, we design and implement a GML repository to store GML documents on the basis of GML schema using spatial database system. GML Schema is converted into directed GML schema graph and the schema mapping technique from directed schema graph to spatial schema is presented. Also, we define the conversion rules on spatial schema to preserve the constraints of GML schema. GML repository using spatial database system is useful to provide the interoperability of geographic information and to store and manage enormous GML documents.

• Journal of The Korean Association of Information Education
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• v.9 no.1
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• pp.79-86
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• 2005

With the rapid advance of computer and communication technology, the amount of data transferred is also increasing more than ever. The recent trend of education systems is connecting related information semantically in different systems in order to improve the utilization of computerized information Therefore, Web-based teaching-learning is developing in the ubiquitous learning direction that learners select and organize the contents, time and order of learning by themselves. That is, it is evolving to provide teaching-learning environment adaptive to individual learners' characteristics (their level of knowledge, pattern of study, areas of interest). This paper proposes the efficient evaluation method of learning contents in a mediator for the integration of heterogeneous information resources. This means that the autonomy of a remote server can be preserved to the highest degree. In addition, this paper proposes the adaptive optimization of learning contents such that available storage in a mediator can be highly utilized at any time. In order to differentiate the recent usage of a learning content from the past, the accumulated usage frequency of a learning content decays as time goes by.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.407-411
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• 2005

KUGI (Korean UniGene Information) database contains the annotation information of the cDNA sequences obtained from the disease samples prevalent in Korean. A total of about 157,000 5'-EST high throughput sequences collected from cDNA libraries of stomach, liver, and some cancer tissues or established cell lines from Korean patients were clustered to about 35,000 contigs. From each cluster a representative clone having the longest high quality sequence or the start codon was selected. We stored the sequences of the representative clones and the clustered contigs in the KUGI database together with their information analyzed by running Blast against RefSeq, human mRNA, and UniGene databases from NCBI. We provide a web-based search engine fur the KUGI database using two types of user interfaces: attribute-based search and similarity search of the sequences. For attribute-based search, we use DBMS technology while we use BLAST that supports various similarity search options. The search system allows not only multiple queries, but also various query types. The results are as follows: 1) information of clones and libraries, 2) accession keys, location on genome, gene ontology, and pathways to public databases, 3) links to external programs, and 4) sequence information of contig and 5'-end of clones. We believe that the KUGI database and search system may provide very useful information that can be used in the study for elucidating the causes of the disease that are prevalent in Korean.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.387-398
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• 2003

Materialized views have received much attention for query performance improvement. They need to be refreshed whenever their underlying data sources are updated. They could be recomputed from scratch or they could be incrementally refreshed by reflecting only those portions of updates that affect them. With emergence of XML as the standard for data exchange on the Web, active research is under way on effectively storing and retrieving XML documents. In this paper, we describe a performance study on the incremental refresh of XML materialized views for the case where XML documents are stored in a relational DBMS, and XML materialized views are maintained with incremental refresh. We describe implementation of a prototype XML storage system that supports XML materialized views and their incremental refresh, and report the performance results obtained with the implemented system through a detailed set of experiments on the incremental refresh of XML materialized views. The results show that the XML view maintenance with incremental refresh outperforms the ordinary view recomputation.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.600-608
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• 2017

Recently data growth rates are growing exponentially according to the rapid expansion of internet. Since users need some of all the information, they carry a heavy workload for examination and discovery of the necessary contents. Therefore information retrieval must provide hierarchical class information and the priority of examination through the evaluation of similarity on query and documents. In this paper we propose an Multi-class support vector machines model based clustering for hierarchical document categorization that make semantic search possible considering the word co-occurrence measures. A combination of hierarchical document categorization and SVM classifier gives high performance for analytical classification of web documents that increase exponentially according to extension of document hierarchy. More information retrieval systems are expected to use our proposed model in their developments and can perform a accurate and rapid information retrieval service.

• The KIPS Transactions:PartD
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• v.12D no.2 s.98
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• pp.247-258
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• 2005

Protein structure is highly related to its function and comparing protein structure is very important to identify structural motif, family and their function. In this paper, we construct an integrated database system which has all the protein structure data and their literature. The structure queries from the web interface are compared with the target structures in database, and the results are shown to the user for future analysis. To constructs this system, we analyze the Flat-File of Protein Data Bank. Then we select the necessary structure data and store as a new formatted data. The literature data related to these structures are stored in a relational database to query the my kinds of data easily In our structure comparison system, the structure of matched pattern and RMSD valure are calculated, then they are showed to the user with their relational documentation data. This system provides the more quick comparison and nice analyzing environment.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.17-26
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• 2005

With the rapidly Slowing information, the research on the effcient information retrieval is increasing. Most of the retrieval systems for domestic cultural assets on the web have adopted a keyword-based search method. Those systems have required users to know the exact information about cultural assets such as name, keyword, etc. However, it is not easy to search the cultural assets with little information or only a remembrance of the shape. In this paper, we propose the retrieval system for cultural assets using both ontology-based and sketch-based search method to solve the Problems of existing systems. Our retrieval system allows users to use both text and sketch for a Query regardless of the type of information about cultural assets and to search in results using the ontology.

• The Journal of Korean Association of Computer Education
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• v.8 no.6
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• pp.113-121
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• 2005

Since documents on the Web are naturally partitioned into many document databases, the efficient information retrieval (IR) process requires identifying the document databases that are most likely to provide relevant documents to the query and then querying the identified document databases. We first introduce a neural net agent for such an efficient IR, and then propose the hierarchically organized multi-agent IR system in order to scale our agent with the large number of document databases. In this system, the hierarchical organization of neural net agents reduced the total training cost at an acceptable level without degrading the IR effectiveness in terms of precision and recall. In the experiment, we introduce two neural net IR systems based on single agent approach and multi-agent approach respectively, and evaluate the performance of those systems by comparing their experimental results to those of the conventional statistical systems.