• 한국빅데이터학회지
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• 제1권2호
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• pp.91-102
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• 2016

전통적인 환경에서 데이터 생명주기는 데이터-정보-지식-지혜 전환과정으로 요약된다. 반면에 빅데이터 환경에서 데이터 생명주기는 데이터-통찰-실행 전환과정으로 요약된다. 이러한 전환과정의 차이점은 데이터 생명주기를 지원하는 데이터 자원 관리에도 변화를 요구한다. 본 논문에서는 전통적인 데이터 자원 관리와 비교하여 빅데이터 환경을 위한 데이터 자원 관리를 연구한다. 특히 빅데이터 자원관리를 위한 주요 구성요소를 제안한다.

• 한국IT서비스학회지
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• 제13권2호
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• pp.185-194
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• 2014

Recently, as value for practical use of big data is evaluated, companies and organizations that create benefit and profit are gradually increasing with application of big data. But specifical and theoretical study about possible risk factors as introduction of big data is not being conducted. Accordingly, the study extracts the possible risk factors as introduction of big data based on literature reviews and classifies according to big data processing, data collection, data storage, data analysis, analysis data visualization and application. Also, the risk factors have order of priority according to the degree of risk from the survey of experts. This study will make a chance that can avoid risks by bid data processing and preparation for risks in order of dangerous grades of risk.

• 한국경영과학회지
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• 제14권1호
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• pp.87-87
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• 1989

There have been lots of research on temporal data management for the past two decades. Most of them are based on some logical data model, especially on the relational data model, although there are some conceptual data models which are independent of logical data models. Also, many properties or issues regarding temporal data models and temporal query languages have been studied. But some of them were shown to be incompatible, which means there could not be a complete temporal data model, satisfying all the desired properties at the same time. Many modeling issues discussed in the papers, do not have to be done so, if they take object-oriented data model as a base model. Therefore, this paper proposes a temporal data model, which is based on the object-oriented data model, mainly discussing the most essential issues that are common to many temporal data models. Our new temporal data model and query language will be illustrated with a small database, created by a set of sample transaction.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.927-935
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• 2007

Currently, Gyeongnam province is executing the social index survey every year to the provincials. But, this survey has the limit of the analysis as execution of the different survey per 3 year cycles. The solution of this problem is data fusion. Data fusion is the process of combining multiple data in order to provide information of tactical value to the user. But, data fusion doesn#t mean the ultimate result. Therefore, efficient analysis for the data fusion is also important. In this study, we present data fusion method of statistical survey data. Also, we suggest application methodology of association rule mining by clustering through data fusion of statistical survey data.

• Journal of the Korean Data and Information Science Society
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• 제21권3호
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• pp.605-619
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• 2010

Caching of frequently accessed data in multi-hop ad hoc environment is a technique that can improve data access performance and availability. Cooperative caching, which allows sharing and coordination of cached data among several clients, can further en-hance the potential of caching techniques. In this paper, we propose a fuzzy cooperative caching scheme in mobile ad hoc networks. The cache management of the proposed caching scheme not only uses adaptively CacheData or CachePath based on data sim-ilarity and data utility, but also uses the replacement manager based on data pro t. Also, the proposed caching scheme uses a prefetch manager. When the TTL of the cached data expires, the prefetch manager evaluates the popularity index of the data. If the popularity index is larger than a threshold, the data is prefetched. Otherwise, its space is released. The performance of the proposed scheme is evaluated analytically and is compared to that of other cooperative caching schemes.

• KEPCO Journal on Electric Power and Energy
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• 제7권1호
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• pp.79-84
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• 2021

The fundamental element to be kept for big data analysis, artificial intelligence technologies and asset management system is a data quality, which could directly affect the entire system reliability. For this reason, the momentum of data cleaning works is recently increased and data cleaning methods have been investigating around the world. In the field of electric power, however, asset data cleaning methods have not been fully determined therefore, automatic cleaning algorithm of asset data for transmission cables has been studied in this paper. Cleaning algorithm is composed of missing data treatment and outlier data one. Rule-based and expert opinion based cleaning methods are converged and utilized for these dirty data.

• Journal of Multimedia Information System
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• 제9권2호
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• pp.171-176
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• 2022

Today, it is imperative to educate students on how to best prepare themselves for the new data driven era of the future. Undergraduate education plays an important role in providing students with more Data Science opportunities and expanding the supply of Data Science talent. This paper surveys and analyzes the curricula of Data Science-related bachelor's degree programs in the United States. The 'required' and 'elective' courses in a curriculum for obtaining a B.S. degree were evaluated by course weight to indicate its necessity. As a result, it was possible to find out which courses were important in Data Science programs and which areas were emphasized for B.S. degrees in Data Science. We found that courses belong to the Data Science area, such as data management, data visualization, and data modeling, were more required for Data Science B.S. degrees in the United States.

• 한국멀티미디어학회논문지
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• 제18권4호
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• pp.524-532
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• 2015

All data created in BigData times is included potentially meaning and correlation in data. A variety of data during a day in all society sectors has become created and stored. Research areas in analysis and grasp meaning between data is proceeding briskly. Especially, accuracy of meaning prediction and data imbalance problem between data for analysis is part in course of something important in data analysis field. In this paper, we proposed data prediction model based on data weights and neural network using R for meaning analysis between data. Proposed data prediction model is composed of classification model and analysis model. Classification model is working as weights application of normal distribution and optimum independent variable selection of multiple regression analysis. Analysis model role is increased prediction accuracy of output variable through neural network. Performance evaluation result, we were confirmed superiority of prediction model so that performance of result prediction through primitive data was measured 87.475% by proposed data prediction model.

• 대한안전경영과학회지
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• 제11권4호
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• pp.221-228
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• 2009

Electronic Product Code Information Services(EPCIS) is an EPCglobal standard for sharing EPC related information between trading partners. EPCIS provides a new important capability to improve efficiency, security, and visibility in the global supply chain. EPCIS data are classified into two categories, master data (static data) and event data (dynamic data). Master data are static and constant for objects, for example, the name and code of product and the manufacturer, etc. Event data refer to things that happen dynamically with the passing of time, for example, the date of manufacture, the period and the route of circulation, the date of storage in warehouse, etc. There are four kinds of event data which are Object Event data, Aggregation Event data, Quantity Event data, and Transaction Event data. This thesis we propose an event-based data model for EPC Information Service repository in RFID based integrated logistics center. This data model can reduce the data volume and handle well all kinds of entity relationships. From the point of aspect of data quantity, we propose a formula model that can explain how many EPCIS events data are created per one business activity. Using this formula model, we can estimate the size of EPCIS events data of RFID based integrated logistics center for a one day under the assumed scenario.

• 한국환경과학회지
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• 제16권10호
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• pp.1127-1137
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• 2007

We focused on effects on data assimilation of simulated wind fields by using upper-air observations (wind profiler and sonde data). Local Analysis Prediction System (LAPS), a type of data assimilation system, was used for wind field modeling. Five cases of simulation experiments for sensitivity analysis were performed: which are EXP0) non data assimilation, EXP1) surface data, EXP2) surface data and sonde data, EXP3) surface data and wind profiler data, EXP4) surface data, sonde data and wind profiler data. These were compared with observation data. The result showed that the effects of data assimilation with wind profiler data were found to be greater than sonde data. The delicate wind fields in complex coastal area were simulated well in EXP3. EXP3 and EXP4 using wind profiler data with vertically high resolution represented well sophisticated differences of wind speed compared with EXP1 and EXP2, this is because the effects of wind profiler data assimilation were sensitively adjusted to first guess field than those of sonde observations.