• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.459-462
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• 2003

Selectivity estimation of queries not only provides useful information to the query processing optimization but also may give users with a preview of processing results. In this paper, we investigate the problem of selectivity estimation in the context of a spatial dataset. Although several techniques have been proposed in the literature to estimate spatial query result sizes, most of those techniques still have some drawback in the case that a large amount of memory is required to retain accurate selectivity. To eliminate the drawback of estimation techniques in previous works, we propose a new method called MW Histogram. Our method is based on two techniques: (a) MinSkew partitioning algorithm that processes skewed spatial datasets efficiently (b) Wavelet transformation which compression effect is proven. We evaluate our method via real datasets. With the experimental result, we prove that the MW Histogram has the ability of providing estimates with low relative error and retaining the similar estimates even if memory space is small.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.384-387
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• 2003

Most of the real world phenomena change over time. The ability to represent and to reason geographic data becomes crucial. A large amount of non-standard applications are dealing with data characterized by spatial, temporal and/or uncertainty features. Non-standard data like spatial and temporal data have an inner complex structure requiring sophisticated data representation, and their operations necessitate sophisticated and efficient algorithms. Current GIS technology is inefficient to model and to handle complex geographic phenomena, which involve space, time and uncertainty dimensions. This paper concentrates on developing a fuzzy spatio-temporal data model based on fuzzy set theory and relational data models. Fuzzy spatio-temporal operators are also provided to support dynamic query.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.455-465
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• 2006

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, and relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze industrial waste database using data mining technique. We use k-means algorithm for clustering and C5.0 algorithm for decision tree and Apriori algorithm for association rule. We can use these outputs for environmental preservation and environmental improvement.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.164-173
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• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, data reduction and variable screening, category merging, etc. We analyze waste database united with local information using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.15-20
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• 2005

We developed a database system to enable efficient and high-throughput transposon analyses in rice. We grow large-scale mutant series of rice by taking advantage of an active MITE transposon mPing, and apply the transposon display method to them to study correlation between genotypes and phenotypes. But the analytical phase, in which we find mutation spots from waveform data called fragment profiles, involves several problems from a viewpoint of labor amount, data management, and reliability of the result. As a solution, our database system manages all the analytical data throughout the experiments, and provides several functions and well designed web interfaces to perform overall analyses reliably and efficiently.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.713-716
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• 2005

In this paper, in order to identify and recognize attack patterns, we propose a Bayesian classification using frequent patterns. In theory, Bayesian classifiers guarantee the minimum error rate compared to all other classifiers. However, in practice this is not always the case owing to inaccuracies in the unrealistic assumption{ class conditional independence) made for its use. Our method addresses the problem of attribute dependence by discovering frequent patterns. It generates frequent patterns using an efficient FP-growth approach. Since the volume of patterns produced can be large, we propose a pruning technique for selection only interesting patterns. Also, this method estimates the probability of a new case using different product approximations, where each product approximation assumes different independence of the attributes. Our experiments show that the proposed classifier achieves higher accuracy and is more efficient than other classifiers.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.273-276
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• 2004

Multidisciplinary Design Optimization (MDO) is an individual and parallel design framework applied in designing large and complex systems. for successful implementation of MDO framework it is essential to manage data in efficient and integrated manner. In this study, we present a case study to implement database to support designing super high temperature vacuum furnace with MDO technology. For that purpose we first extract required data based on the analysis of design process and then data flows between different programs are analyzed. Finally an E-R diagram is presented to design database schema.

• Journal of the Korean Society of Systems Engineering
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• v.7 no.2
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• pp.39-43
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• 2011

Modern naval ships are large complex systems with the number of requirements ranges from thousands to tens of thousands. To build a quality ship, the satisfaction of the requirements should be traced. In most shipbuilding projects it is almost impossible to manage all the requirements without a proper CASE (computer aided systems engineering) tool. And for effective management of the shipbuilding project, the integrated database for technical data is very important. This paper describes how the requirements are managed, and the integrated database is built in the naval shipbuilding industry in Korea.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.681-684
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• 2004

In order for effective damage prevention, it is necessary to have some idea of when, where, why and what kind of natural disasters may strike, and how large they may be. In this study, I made a database which can be use for GIS to facilitate multivariate analysis of presently available data for Shizuoka prefecture. This analysis can map out likely natural disaster locations and causes. Using the result of this analysis for GIS, a regional range of the disaster categorized by factors can be shown and analyzed visually and easily updated when a disaster occurs in the future.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.500-504
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• 2007

Database are developed to control measured settlement data under construction in Gyungbu High Speed Railway from Daegu to Busan. This means that data having different type at different site could be managed in a unified way. The database includes algorithm to evaluate embankment settlement with settlement data at the surface of embankment and ground settlement data. And also, it has a function to analyse the causes of large settlement over allowable level and high settlement speed based on the log data, embankment specification, physical characteristics of embankment materials.