• The Journal of the Korea Contents Association
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• v.13 no.9
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• pp.410-417
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• 2013

Long-term behavior analysis considering construction sequence should be performed in the design and the actual construction of reinforced tall buildings. Most of the analytical studies on this subject, however, has not been applied directly to the structural design and the construction caused by the simple approach. As the axial force redistribution of shores and columns is time-dependent, the actual construction sequence with the placement of concrete, form removal, reshoring, shore removal, and the additional load application is very important. Object-oriented analysis program considering construction sequence, especially time-dependent deformation in early days, is developed. This system is composed of input module, database module, database store module, analysis module, and result generation module. Linkage interface between the central database and each of the related module is implemented by the visual c# concept. Graphic user interface and the relational database table are supported for user's convenience.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.5
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• pp.501-509
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• 2002

SGML(ISO 8879) has been proliferated to support various document styles and to transfer documents into different platforms. SGML documents have logical structure information in addition to contents. As SGML documents are widely used, there is an increasing need for database storage and retrieval system using the logical structure of documents. However. traditional search engines using document indexes cannot exploit the logical structure. In this Paper, we have developed an SGML document storage system, which is DTD-independent and store the document type and the document instance separately by using Grove which is the document model for DSSSL and HyTime. We have used the Object Store, an object-oriented DBMS, to store the structure information appropriately without any loss of structural information. Also, we have supported a index structure for search efficiency like the relational DBMS, and constructed an effective user interface which combines content-based search with structure-based search.

• Journal of KIISE:Databases
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• v.31 no.6
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• pp.585-595
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• 2004

A query processing is a critical issue in data warehouse environment since queries on data warehouses often involve hundreds of complex operations over large volumes of data. Data warehouses therefore build a large number of materialized views to increase the system performance. Which views to materialized is an important factor on the view maintenance cost as well as the query performance. The goal of materialized view selection problem is to select an optimal set of views that minimizes total query response time in addition to the view maintenance cost. In this paper, we present an efficient solution for the materialized view selection problem. Although the optimal selection of materialized views is NP-hard problem, we developed a feasible solution by utilizing the characteristics of relational operators such as join, selection, and grouping.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.298-305
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• 2016

Various NoSQL databases are more excellent to process a large amount of big data than existing relational databases such as MySQL, PostgreSQL and Oracle. Among widely used NoSQL databases, performance of HBase, Cassandra, MongoDB and Redis was comparatively assessed. For distributed processing of a large amount of data, 12 servers were connected through switching hub and Ubuntu was installed as operating system. As for benchmark tool, YCSB was applied. Read and update ratios changed from 50% and 50%, 95% and 5% and finally, 100% and 0% and each of them was assessed as 200,000 commands developed into 1,200,000 commands for each case. Cassandra was most excellent with transaction processing per second while MongoDB was most excellent with the number of processes carried out per unit time.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.482-487
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• 2015

This paper presents the results and the process of extracting correlations between events of prescriptions and examinations using graph-modeling and node centrality measures on a medical dataset of 11,938 patients with diabetes mellitus. As the data is stored in relational form, RDB2Graph framework was used to construct effective graph models from the data. Personalized PageRank was applied to analyze correlation between prescriptions and examinations of the patients. Two graph models were constructed: one that models medical events by each patient and another that considers the time gap between medical events. The results of the correlation analysis confirm current medical knowledge. The paper demonstrates some of the note-worthy findings to show the effectiveness of the method used in the current analysis.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.1
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• pp.1-8
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• 2002

XML has been proposed as an international standard for organizing and exchanging a great diversity of the Web data. It is important to retrieve components of stored XML documents that are needed by a wide variety of applications. In this paper, we suggest a method to store XML documents and to retrieve an XML data. In other words, we suggest the method of retrieving XML data is using XML -QL. So we need to mapping XML-QL to SQL translator on top of an RDBMS. The contributions of this paper include, besides the detailed design and implementation of the translator, demonstration of feasibility of such a translator, and a comprehensive classification of XML queries and their mappings to SQL relational queries.

• Journal of KIISE:Databases
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• v.29 no.2
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• pp.89-98
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• 2002

As XML has become an emerging standard for information exchange on the World Wide Web it has gained attention in database communities to extract information from XML seen as a database model. Recently, many researchers have addressed the problem of storing XML data and processing XML queries using traditional database engines. Here, most of them have used relational database systems. In this paper, we show that OODBSs can be another solution. Our technique generates an OODB schema from DTDs and processes XML queries, Especially, we show that the semi-structural part of XML data can be represented by the 'inheritance' and that this can be used to improve query processing.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.521-526
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• 2016

As on-line informal text data have massive in its volume and have unstructured characteristics in nature, there are limitations in applying traditional relational data model technologies for data storage and data analysis jobs. Moreover, using dynamically generating massive social data, social user's real-time reaction analysis tasks is hard to accomplish. In the paper, to capture easily the semantics of massive and informal on-line documents with unsupervised learning mechanism, we design and implement automatic topic extraction systems according to the mass of the words that consists a document. The input data set to the proposed system are generated first, using N-gram algorithm to build multiple words to capture the meaning of the sentences precisely, and Hadoop and Spark (In-memory distributed computing framework) are adopted to run topic model. In the experiment phases, TB level input data are processed for data preprocessing and proposed topic extraction steps are applied. We conclude that the proposed system shows good performance in extracting meaningful topics in time as the intermediate results come from main memories directly instead of an HDD reading.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.984-987
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• 2018

In an Internet of Thing (IoT) environment, entities with different attributes and capacities are going to be connected in a highly connected fashion. Specifically, not only the mechanical and electronic devices but also other entities such as people, locations and applications are connected to each other. Understanding and managing these connections play an important role for businesses, which identify opportunities for new IoT services. Traditional approach for storing and querying IoT data is used of a relational database management system (RDMS) such as MySQL or MSSQL. However, using RDMS is not flexible and sufficient for handling heterogeneous IoT data because these data have deeply complex relationships which require nested queries and complex joins on multiple tables. In this paper, we propose a graph model for constructing a graph database of heterogeneous IoT data. Graph databases are purposely-built to store highly connected data with nodes representing entities and edges representing the relationships between these entities. Our model fuses social graph, spatial graph, and things graph, and incorporates the relationships among them. We then present a case study which applies our model for representing data from a Smart Campus using Neo4J platform. Through the results of querying to answer real questions in Smart Campus management, we show the viability of our model.

• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.1951-1959
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• 1998

In this paper, we propose an efficient feature extaetion method for content-based approach and implement an image retrieval system in the Oracle database. First, we estract color feature by the modified Stricker's method from input images, and this color feature and ART2 neural network are used for the rough classification of images. Next, we extract texture feature using wavelet transform, and finally exeute the detailed classification on the rough classified images from the previous step. Exsing the proposed feature extraction methods, we implement a useful image retrieval system by Extended SQI, statement on the relational database. The proposed system is implemented on the Oracle DBMS, and in the experimental results with 200 sample images, it shows the retrieval rate 90% and 81% in Recall and Precision, respectively.