• The Journal of Natural Sciences
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• v.8 no.1
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• pp.109-114
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• 1995

For effective management of statistical database, statistical summary information must be provided by accessing directly the precomputed summary data from summary database to store and manage meta database for supporting statistical analysis and providing users with statistical summary information. In order to support effectively the use of summary database, we do the design and implementation of meta database and manager having a hierarchical structure as a data dictionary/directory and operation method is presented.

• The Journal of Information Technology and Database
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• v.4 no.2
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• pp.33-45
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• 1998

Database summarization is the procedure to obtain generalized and representative descriptions expressing the content of a large amount of database at a glance. We present a top-down summary refinement procedure to discover database summaries. The procedure exploits attribute concept hierarchies that represent ISA relationships among domain concepts. It begins with the most generalized summary and proceeds to find more specialized ones by stepwise refinements. This top-down paradigm reveals at least two important advantages compared to the previous bottom-up methods. Firstly, it provides a natural way of reflecting the user's own discovery preference interactively. Secondly, it does not produce too large intermediate result that makes it hard for the bottom-up approach to be applied in practical environment. The proposed procedure can also be easily extended for distributed databases. Information content measure of a database summary is derived in order to identify more informative summaries among the discovered results.

• Genomics & Informatics
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• v.4 no.4
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• pp.173-181
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• 2006

Pattern finding is one of the important tasks in a protein or DNA sequence analysis. Alignment is the widely used technique for finding patterns in sequence analysis. BLAST (Basic Local Alignment Search Tool) is one of the most popularly used tools in bio-informatics to explore available DNA or protein sequence databases. BLAST may generate a huge output for a large sequence data that contains various sequence patterns. However, BLAST does not provide a tool to summarize and analyze the patterns or matched alignments in the BLAST output file. BLAST lacks of general and robust parsing tools to extract the essential information out from its output. This paper presents a pattern summary system which is a powerful and comprehensive tool for discovering pattern structures in huge amount of sequence data in the BLAST. The pattern summary system can identify clusters of patterns, extract the cluster pattern sequences from the subject database of BLAST, and display the clusters graphically to show the distribution of clusters in the subject database.

• The Journal of Natural Sciences
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• v.5 no.2
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• pp.25-32
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• 1992

Statistical database(SDB) are large database primarily collected for purpose of statistical analysis. Commerical database management systems have not been widely used for SDB because of the efficiency problem of storage and access of those systems for SDB. In this paper, we propose SDB management method to use a front-end system to a Relatianal Datebase Management System (RDBMS). We do the design of SM-F system (Stasticical database Management as Front-end system) as a front-end system to a RDBMS. In the system, we use GROS model specially proposed for SDB, and store and manage summary database and meta database to support statistical analysis and to provide users with statistical summary information.

• Journal of Pharmaceutical Investigation
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• v.38 no.6
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• pp.429-432
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• 2008

Drug delivery systems (DDS) have entered mainstream in the pharmaceutical industry in the recent years. Major pharmaceutical companies as well as small or medium-sized biotechnology companies are developing various DDS-based products. We have established Drug Delivery System Company Database, which is an online searchable database of companies that develop DDS-based products and technologies or supply formulations and/or materials. Company summary, products and key technologies are listed in the database. DDS technology fields also include administration routes and indications of drugs. DDS terminologies, Statistical analysis, Useful Links, Glossary and Comments pages are also provided.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2080-2085
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• 2008

KEPCO's AMR (Automatic Meter Reading) is a system that performs the real-time inspection and management of the 15-minute load profile of electric power consumption through a wired and/or wireless network such as CDMA. It has been utilized widely for real-time collection and data analysis. So far, KEPCO has focused on establishing wireless networks using CDMA and collecting data in real time but failed to consider sufficiently performances that can improve the quality of the original data required in terms of data utilization as well as establish the summary information. In this paper, we are going to show the functions that improve data quality by recording the final renewal time of any erroneous data and maintaining such data lists to use them in the rebuilding of summary information. The goals are to reduce any load applied mainly on the DBMS (Database Management System) of AMR, to enable the real-time performance of establishment in the summary information, and to obtain high-quality inspection data. The performance evaluation result has revealed a 10-fold improvement compared to the traditional disk-based DBMS system when the summary information is established.

• Journal of Korean Academy of Nursing Administration
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• v.7 no.3
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• pp.571-588
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• 2001

Purpose : This study was to develop the nursing database for gastric cancer patients for clinical application. Method : Nursing data that development of this data base is comprehensive connected with gastric cancer patient nursing process frame to foundation as classification. Result : Each stage was processed based on the System Development Life Cycle. At the Strategy Planning stage, gastric cancer patient nursing process were analyzed. At the system Analysis Stage, database flowchart was drawn up based on frame of nursing process was drawn up. At the system Design Stage, a system was developed based on the flowchart and named the Nursing Database. The Nursing Database consisted of the patient's Basic Information, Patient's Nursing History, Discharge summary, Nursing Assessment, Nursing Diagnosis, Nursing Intervention/activity, Nursing Evaluation, Statics, Code Registration. Each element in flowchart was coded and made into a database. Nursing Assessment classified according to Gorden's Health Pattern Typology, and nursing diagnosis draws the standard 27 name of Hanguls and connected with nursing assessment. Nursing intervention and nursing activity draw 192 of thing that present in NIC, connected this with nursing assessment. Nursing evaluation is linked with nursing assessment, diagnosis and intervention by achievement availability of nursing goals. Conclusion : The biggest advantage of this database nursing process that can manage nursing information exactly and rapidly to foundation be.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.12a
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• pp.69-74
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• 2004

Selectivity estimation for spatial query is very important process in finding the most efficient execution plan. Many works have been performed to estimate accurately selectivity. Although they deal with some problems such as false-count, multi-count, they require a large amount of memory to retain accurate selectivity, so they can not get good results in little memory environments such as mobile-based small database. In order to solve this problem, we propose a new technique called MW histogram which is able to compress summary data and get reasonable results. It also has a flexible structure to react dynamic update. The experimental results showed that the MW histogram has lower relative error than MinSkew histogram and gets a good selectivity in little memory.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.766-768
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• 2003

Selectivity estimation for spatial query is curial in Spatial Database Management Systems(SDBMS). Many works have been performed to estimate accurate selectivity. Although they deal with some problems such as false-count, multi-count arising from properties of spatial dataset, they can not get such effects in little memory space.* Therefore, we need to compress spatial dataset into little memory. In this paper, we propose a new technique called MW Histogram which is able to compress summary data and get reasonable results. Our method is based on two techniques:(a)MinSkew partitioning algorithm which deal with skewed spatial datasets. efficiently (b) Wavelet transformation which compression effect is proven. We evaluate our method via real datasets. The experimental result shows 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.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.702-714
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• 2001

As Internet technology evolves, there is growing need of Internet queries involving multiple information sources. Efficient processing of such queries necessitates the integrated summary data that compactly represents the data distribution of the entire database scattered over many information sources. This paper presents an efficient method of managing the integrated summary data based on the wavelet transform and addresses Internet query processing using the integrated summary data. The simplest method for creating the integrated summary data would be to summarize the integrated data sidtribution obtained by merging the data distributions in multiple information sources. However, this method suffers from the high cost of transmitting storing and merging a large amount of data distribution. To overcome the drawbacks, we propose a new wavelet transform based method that creates the integrated summary data by merging multiple summary data and effective method for optimizing Internet queries using it A wavelet transformed summary data is converted to satisfy conditions for merging. Moreover i the merging process is very simpe owing to the properties of the wavelet transform. we formally derive the upper bound of the error of the wavelet transformed intergrated summary data. Compared with the histogram-based integrated summary data the wavelet transformedintegrated summary data provesto be 1.6~5.5 time more accurate when used for selectivity estimation in experiments. In processing Internet top-N queries involving 56 information sources using the integrated summary data reduces the processing cost to 1/44 of the cost of not using it.