Earticle

클라우드 환경에서 기업은 의사결정이나 기업의 정책을 결정하기 위해 데이터 통합 및 분석이 필요하다. 그 러나 이 환경에서 새로운 시스템이 추가되면 데이터 통합 측면에서 기존 시스템이 가진 이질적인 특성 때문에 많은 비용과 시간이 필요로 하게 된다. 따라서 본 논문에서 멀티 데이터베이스 서비스를 위하여 DBaaS 허브 시스템을 제안한다. 이러한 DBaaS는 서로 다 른 데이터베이스를 요구할 수도 있으며, 연관성이 있는 서비스를 위하여 데이터를 통합해야할 필요가 있다. DBaaS의 데이터 상호운용성 문제를 해결하기 위해 온톨로지를 이용한 메타 쿼리를 제안한다. 메타 쿼리는 실제 데 이터에 접근하기 위한 쿼리가 아닌 상위의 쿼리이다. 이는 실제 데이터베이스에 접근할 때 온톨로지를 통하여 변환 된 질의로 접근함으로써 데이터 통합을 기할 수 있다. 또한 메타 데이터를 이용하여 문서 지향 데이터베이스 시스템 을 구축한다.

In the cloud environment, the company needs data integration and analysis to make decision and policy. If new system is added to this environment, a lot of time and cost is needed due to disparate properties among systems when data is integrated. Therefore, in this paper, we propose a DBaaS hub system for multi-database service. The DBaaS may require a different database and need data integration for relevant service. Using the ontology, we propose a metadata query to resolve the interoperability issues between data of DBaaS. The meta-query is not a query to access the real data, but the query for the upper level. This method provides data integration by accessing the data with the converted query through an ontology when we access the actual database.We also constructs a document-oriented database system using the metadata.

Database classification is an important preprocessing step for the multi-database mining (MDM). In fact, when a multi-branch company needs to explore its distributed data for decision making, it is imperative to classify these multiple databases into similar clusters before analyzing the data. To search for the best classification of a set of n databases, existing algorithms generate from 1 to ($n^2-n$)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification are subsets of clusters in the next classification), existing algorithms generate each classification independently, that is, without taking into account the use of clusters from the previous classification. Consequently, existing algorithms are time consuming, especially when the number of candidate classifications increases. To overcome the latter problem, we propose in this paper an efficient approach that represents the problem of classifying the multiple databases as a problem of identifying the connected components of an undirected weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our algorithm against existing works and that it overcomes the problem of increase in the execution time.

In this study, we proposed an architectural concept for Database Switching Architecture Based Distributed Simulation Service for combat aircraft training simulators. Training simulation service data is provided by the database switching architecture, and flight simulation environment data is provided by the Database Switching Architecture Based Distributed Simulation Service. In the LVC Training Environment, integration and interoperability are two very important factors for the Distributed Simulation Service. By the suggested Architecture Concept, each training simulator can get data from the dual database by accessing the online Data Base Switching Architecture Based Distributed Simulation Service. The suggested architecture concept will be helpful to the LVC environment and training simulation.

Objectives: To establish a system for integrated risk assessment of EDCs in Korea, infrastructure for providing toxicity data of ecological media should be established. Some systems provide soil ecotoxicity databases along with aquatic ecotoxicity information, but a well-structured ecotoxicity database system is still lacking. Methods: Aquatic and soil ecotoxicological information were collected by a toxicologist based on a human readable data (HRD) format for collecting ecotoxicity data that we provided. Among these data, anomalies were removed according to database normalization theory. Also, the data were cleaned and encoded to establish a machine-readable data (MRD) ecotoxicity database system. Results: We have developed a multi-purpose ecotoxicity database model focusing on EDCs, ecological species, and toxic effects. Also, we have constructed a web-based data searching system to retrieve, extract, and download data with greater availability. Conclusions: The results of our study will contribute to decision-making as a tool for efficient ecological risk assessment of EDCs in Korea.

효과적인 데이터 분석 및 활용을 위해서는 빅 데이터를 내부 데이터와 유연하게 연계할 수 있는 방안이 필요하다. 빅 데이터 분석 정보를 내부 정보시스템과 연계시키기 위한 방안으로서 본 연구는 다차원 이벤트 온톨리지를 제시하였다. 이를 위해서 먼저 빅 데이터 분석 정보를 이벤트 모형을 사용하여 온톨리지로 표현하고, 다차원 데이터베이스 또한 OWL-DL 온톨리지로 변환하여 표현하였다. 다차원 이벤트 온톨리지에서 빅 데이터 분석정보들은 차원 계층구조를 통하여 다차원 데이터베이스에 저장된 모든 개체들에게 공유되는데, 본 연구에서는 이를 이벤트의 하향공유, 상향 공유 및 복합 이벤트 공유로 구분한다. 이들 정보공유 유형별로 빅 데이터 분석 정보의 공유 및 활용 방안들을 제시하였으며, Protege를 사용하여 시험적으로 구현하였다. 본 연구는 외부의 빅 데이터 분석 정보를 내부의 다차원 데이터베이스와 연계하는 방안 을 실험적으로 제시하였다는 점에서 의의를 가진다고 할 수 있다.

As external data have become important corporate information resources, there are growing needs to combine them with internal data. This paper proposes an ontology-based scheme to combine external data with multi-dimensional databases, which shall be called multi-dimensional event ontology. In the ontology, external data are represented as events. Event characteristics such as actors, places, times, targets are linked to dimensions of a multi-dimensional database. By mapping event characteristics to database dimensions, external event data are shared via multi-dimensional hierarchies. This paper proposes rules to synchronize information sharing in multi-dimensional event ontology such as upward event information sharing, downward event information sharing and complex event information sharing. These rules are implemented using Protege. This study has a value in suggesting Big Data information sharing processes using an event database framework.

Objectives: Since 1998, the Korea National Health and Nutrition Examination Survey (KNHANES) has been conducted in order to investigate the health and nutritional status of Koreans. The food intake data of individuals in the KNHANES has also been utilized as source dataset for risk assessment of chemicals via food. To improve the reliability of intake estimation and prevent missing data for less-responded foods, the structure of integrated long-standing datasets is significant. However, it is difficult to merge multi-year survey datasets due to ineffective cleaning processes for handling extensive numbers of codes for each food item along with changes in dietary habits over time. Therefore, this study aims at 1) cleaning the process of abnormal data 2) generation of integrated long-standing raw data, and 3) contributing to the production of consistent dietary exposure factors. Methods: Codebooks, the guideline book, and raw intake data from KNHANES V and VI were used for analysis. The violation of the primary key constraint and the $1^{st}-3rd$ normal form in relational database theory were tested for the codebook and the structure of the raw data, respectively. Afterwards, the cleaning process was executed for the raw data by using these integrated codes. Results: Duplication of key records and abnormality in table structures were observed. However, after adjusting according to the suggested method above, the codes were corrected and integrated codes were newly created. Finally, we were able to clean the raw data provided by respondents to the KNHANES survey. Conclusion: The results of this study will contribute to the integration of the multi-year datasets and help improve the data production system by clarifying, testing, and verifying the primary key, integrity of the code, and primitive data structure according to the database normalization theory in the national health data.

In SaaS, since the service provider may be un-trusted, it is essential for tenants to enable query result correctness and completeness. However, existing data authentication methods can not fit well with the customized multi-tenants sharing storage mode. This paper put forward a multi-tenant data authentication model (TCDA). TCDA is a composite structure that constructs pivot authentication tree (α-tree) on the pivot table and combines it with signature set (β-set) built on sparse table to ensure that malicious insiders can't modify the data in pivot table and sparse table. The main contribution of TCDA is it can guarantee the tenant query result in one tree travels and return the verification object corresponding to the result on pivot table and sparse table. And in this paper, we propose an improved TCDA model to minimize the processing overhead through appending aggregation signature of the node descendants to the internal α-tree node. We demonstrate effectiveness of our model compare with MHT and DSAC through the experiment.

Object: With the continuous advancement of information technology, more and more systems use database to store basic data, the security of data is an important part of the design of the business system. Method: In order to effectively improve the viability of data, this paper proposes a kind of multi point and multi hop database remote disaster recovery and backup technology. Process: Based on the in-depth analysis of the functions and demand of database, this paper introduces the working principles and key technologies of disaster recovery technology, describes the principles and realization process of multi point and multi hop database remote disaster recovery and backup technology, and carries on the experimental analysis. Conclusion: Theoretical analysis and experimental results show that this method is an effective new way of database remote disaster recovery and backup. So this technology has many advantages, such as multi point and multi hop backup, good real-time performance, fine backup granularity and so on.

In this paper, a semantic based multi-granular lock model for object-oriented database systems is proposed. It addresses the concurrency control issues related to all types of transactions to an object: run time transactions and design time transactions. In the case of run time transactions, it ensures consistency while providing fine granularity. In the case of design time transactions, the proposed work has the following features: First, it provides separate lock modes for all types of design time operations. Second, it provides fine granularity for design time transactions. Third, it reduces deadlocks due to lock escalation. The proposed work shows how concurrency can be maximized while ensuring consistency for all types of transactions. A simulation model is constructed to evaluate the performance of the proposed work. This model is used to compare the proposed work with two existing techniques. The performance results show that proposed scheme is better than existing works.

Inconsistent data in database occurs due to increasing number of data. A suitable technique is needed to reduce inconsistent data from database. In this paper, fuzzy multi attribute decision making was chosen to reduce inconsistent data from database. This technique contains 5 steps which are deriving quality vector, scale the quality matrix, compute weighted Euclidean distance and select final alternative. An application was developed using java and oracle technology. Sample data was selected for experiments purposes. The result indicates fuzzy multi attribute decision making is a suitable technique in reducing inconsistent data from database. Algorithm in fuzzy multi attribute decision making is able to find out a correct object.

A learning system was built into an on-line, multi-institutional radiotherapy database, where the treatment history records and the results in each institution were integrated, each radiotherapy planning was supported, and it led to the improvement in treatment results.

This study sought to describe and to evaluate the characteristics of the Health Screening Records Database (HSRD) of the KoreaAssociation of Health Promotion as a data source for epidemiologic studies. The HSRD was compared to a National Health InsuranceService-Health Screening Cohort (NHIS-HEALS) database for 2015. Common variables between the databases were selected,and sex-based analyses were conducted. The HSRD showed statistical concordance when NHIS-HEALS estimates fell within theHSRD estimate’s 95% confidence interval. The HSRD and NHIS-HEALS included 946461 and 111690 participants in health screeningprograms, respectively. Compared to the NHIS-HEALS, the HSRD had more female (55.2% vs. 42.6%) but fewer older adult participants(34.4% vs. 51.2%). Virtually all variables had clinical concordance, with some having statistical concordance as well, amongboth general and life-transition program participants. The HSRD comprised more clinical information over a wider age range incontrast to the NHIS-HEALS, while showing clinical concordance. Providing more comprehensive clinical data, the HSRD mayserve as an alternative resource for epidemiologic studies.

We present a multi-dimensional reduction method of the surveyed cube database obtained using a single- dish radio telescope in Taeduk Radio Astronomy Observatory (TRAO). The multibeam receiver system installed at the 14 m telescope in TRAO was not optimized at the initial stage, though it became more stabilized in the following season. We conducted a Galactic Plane survey using the multibeam receiver system. We show that the noise level of the first part of the survey was higher than expected, and a special reduction process seemed to be definitely required. Along with a brief review of classical methods, a multi-dimensional method of reduction is introduced; It is found that the 'background' task within IRAF (Image Reduction and Analysis Facility) can be applied to all three directions of the cube database. Various statistics of reduction results is tested using several IRAF tasks. The rms value of raw survey data is 0.241 K, and after primitive baseline subtraction and elimination of bad channel sections, the rms value turned out to be 0.210 K. After the one-dimensional reduction using 'background' task, the rms value is estimated to be 0.176 K. The average rms of the final reduced image is 0.137 K. Thus, the image quality is found to be improved about 43% using the new reduction method.

Purpose The International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) and the Memorial Sloan Kettering Cancer Center (MSKCC) risk models were developed predominantly with clear cell renal cell carcinoma (RCC). Accordingly, whether these two models could be applied to metastatic non-clear cell RCC (mNCCRCC) as well has not been wellknown and was investigated herein. Materials and Methods From the Korean metastatic RCC registry, a total of 156 patients (8.1%) with mNCCRCC among the entire cohort of 1,922 patients were analyzed. Both models were applied to predict first-line progression-free survival (PFS), total PFS, and cancer-specific survival (CSS). Results The median first-line PFS, total PFS, and CSS were 5, 6, and 24 months, respectively. The IMDC risk model reliably discriminated three risk groups to predict survival: the median firstline PFS, total PFS, and CSS for the favorable, intermediate, and poor risk groups were 9, 5, and, 2 months (p=0.001); 14, 7, and 2 months (p < 0.001); and 41, 21, and 8 months (p < 0.001), all respectively. The MSKCC risk model also reliably differentiated three risk groups: 9, 5, and, 2 months (p=0.005); 10, 7, and 3 months (p=0.002); and 50, 21, and 8 months (p < 0.001), also all respectively. The concordance indices were 0.632 with the IMDC model and 0.643 with the MSKCC model for first-line PFS: 0.748 and 0.655 for CSS. Conclusion The current IMDC and MSKCC risk models reliably predict first-line PFS, total PFS, and CSS in mNCCRCC.

한 도메인 내의 통합검색시스템에서 메타데이터를 이용하여 분산되어있는 데이터를 공유하고 표준화하여 정보를 통합검색 할 수 있다. 하지만 메타데이터를 특정 도메인에 맞게 설계하여 메타데이터 레포지스토리로 구축되어진 조직 간에 데이터요소 의미, 구조, 형식상의 이질적인 문제들이 발생하여 통합 검색하기 어렵다. 따라서 본 눈문에서는 XMDR(eXtended Metadata Registry)을 이용한 멀티데이터베이스 통합 검색 시스템을 제안한다. 제안된 시스템은 레거시 데이터 시스템들 간의 협업에 필요한 데이터를 공유 및 교환하는데 실시간으로 변화하는 데이터를 일관성 있게 유지할 수 있으며 통합 검색시 단일 인터페이스를 제공하여 각 시스템의 독립성을 유지하면서 데이터의 투명성과 가용성을 향상 시킬 수 있다.

멀티데이터베이스를 구축하기 위해서는 전역스키마 (global schema) 구축이 필수적이며, 지역스키마(local schema) 간 충돌 해결과 스키마 통합 정보는 질의 처리 및 질의 결과를 사용자에게 보여주기 위해 필요하다. 전역스키마 생성 및 충돌 해결 방법은 많이 연구가 되어 왔다. 하지만, 스키마 통합을 위한 정보가 어떻게 표현되고, 어떻게 사용되는지에 대한 연구는 부족하다. 본 논문에서는 지역스키마와 전역스키마를 XML DTD 및 XML 문서로 표현하고, 이를 이용하여 멀티데이터베이스를 위한 스키마 통합 방법을 제안한다. 스키마 통합정보와 멀티데이터베이스와 지역데이터베이스들간 데이터교환을 XML을 이용함으로써 이기종간 데이터 교환이 쉽고, 멀티데이터베이스 시스템의 확장 및 다른 시스템으로의 전환이 용이하게 된다.

데이터베이스 응용 시스템간에 공유되는 자료가 많아지고 웹 응용 시스템이 일반화됨에 따라서, 웹 상에서도 이기종 데이터베이스 시스템들에 접근하여 질의를 처리하는 방법이 요구되고 있다. 이에 본 논문에서는 이기종 데이터베이스의 자료를 쉽게 처리하는 다중 이기종 데이터베이스 미들웨어 시스템을 웹 환경에서 사용하게 하기 위한 웹 클라이언트를 제안하고, 이 웹 클라이언트가 다수의 전역 데이터베이스에 접근할수 있도록 여러사용자에 대한 세션을 관리하는 방법을 제안한다.

본 연구의 목적은 벡터 도메인 안에 대규모 도로 선형 사상을 대상으로 실시간 데이터 변경, 관리가 가능한 네트워크의 다중 표현 데이터베이스 모델을 구축하는 것이다. 즉, 최상위 레벨의 네트워크 데이터로부터 이에 대응하는 하위 베이스 네트워크 데이터로 순차적으로 데이터 통합과 자동 매칭을 수행하는 상의하달 방식(top-down)을 기초로 하는 프레임워크를 제시하며, 이를 통해 변화 가능한 축척(variable-scale)의 지도를 생성하는 모델을 제안하였다. 구현된 MRDB(Multi-Resolution Database) 모델을 차량 항법 서비스에 적용하여 실제 동적 경로 안내 시스템에 활용 가능함을 확인할 수 있었다.

The aim of this paper is to come up with a methodology of constructing an efficient model for multiple representations which can manage and reconcile real-time data about large-scale roads in Vector Domain. In other words, we suggested framework based on a bottom-up approach, which is allowed to integrate data from the network of the lowest level sequentially and perform automated matching in order to produce variable-scale map. Finally, we applied designed multi-LoD model to in-vehicle application.

A framework of multi layered database is proposed for the integrated operation of civil infrastructure information in this study. The multi-layered database is a logically integrated database composed of standardized information layers. The framework of multi-layered database is defined by three axes, national assets, lifetime, and data levels. The axis of national assets indicates civil infrastructures such as bridges, dams, tunnels and power plants that can be considered as national key structures. The axes of lifetime and data levels indicate the standardized information layers generated from the life-phase of civil infrastructure and the priority of data in the information layers, respectively. The standardized information layers are basically composed of reusable data sets defined by information models. A prototype of standard database for steel bridges is constructed based on the framework as a proof of concept. Demonstration examples such as data consistency check and automatic generation of a FEA model show that the proposed concept can assure the sustainable interoperability of civil infrastructure information as well as design information of steel bridges.

다양한 축척의 자료를 컴퓨터 데이터베이스에 구축한 후, 필요한 축척의 정보를 검색하여 이용하는다축척 지리자료는 연구자의 필요에 맞는 축척의 자료를 검색하여 분석할 수 있다는 장점이 있다. 특히 래스터 자료의 경우, 자료의 정확도와 저장 공간이라는 두 가지 관점에서 축척은 주요한 관심대상 중의 하나이다.본 연구에서는 래스터 지리자료를 대상으로 다양한 축척의 자료를 구성할 수 있는 기법들을 살펴보고, 데이터베이스 생성 시스템의 시안을 구현하였다. 다양한 해상도의 래스터 자료를 구축하고 관리하기 위한 포맷들을 검토하였고, 다양한 축척의 래스터 자료를 생성하는 기법들을 검토하였다. 이들을 종합하여 다축척 래스터지리자료 생성 시스템을 시험 제작하고, 실제 지리자료를 이용하여 변환하였다. 자료의 유형별로 해상도가 변화하여도 자료의 분포특성을 유지하는 효과적인 다축척 자료생성 시스템을 제시하였다.

The data with a multiple scale can be built into multi-scale database. With this database, we can queryor analyse the geographic data at the various scale. In the raster data, the scale or spatial resolution is the maintheme at the point of accuracy and data storage. This study aims to explore the techniques for building the multi-scale rater database and to implement the prototype system to generate the multi-scale database. The format andtransformation rule for the multiple scale data was inspected. And the prototype of multi-scale database generatingsystem was designed and implemented. The multi-scale data which was generated by this study, can maintain thespatial pattern at the original scale.