• Journal of Korea Society of Digital Industry and Information Management
• /
• v.6 no.2
• /
• pp.19-27
• /
• 2010

Historical archived traffic data management system enables a long term time-series analysis and provides data necessary to acquire the constantly changing traffic conditions and to evaluate and analyze various traffic related strategies and policies. Such features are provided by maintaining highly reliable traffic data through scientific and systematic management. Now, the management systems for massive traffic data have a several problems such as, the storing and management methods of a large volume of archive data. In this paper, we describe how to storing and management for the massive traffic data and, we propose methodology for logical and physical architecture, collecting and storing, database design and implementation, process design of massive traffic data.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• 2007.11a
• /
• pp.312-315
• /
• 2007

This study is aimed at developing a web-based management system of maintenance historical data, for effective maintenance performance and smooth exchange of information and communication between public facilities and public educational facilities managers. The current management state of maintenance historical data and its business process were investigated and analyzed, through interviewing with the persons in charge of managing each facility, by recognizing problems of maintenance historical data management that is able to be applied as a basis to judge facility deterioration and performance. In addition, this study suggests a maintenance historical data system of which DB may be applied to the practical business, by reflecting requests of the persons in charge of managing facilities, setting up the scope of system usage by facility, business phase and level of facility managers, and establishing a maintenance business process system.

• Journal of the Korea Institute of Building Construction
• /
• v.4 no.4
• /
• pp.117-126
• /
• 2004

Construction industry is faced with the problems such as the quickly changeable circumstance and increasing construction companies due to regulation mollification of company registration. In order to overcome these problems, new estimation system based on historical estimation cost is ready to introduce by government step by step. But the time of transition for estimation system causes another problems such as chaos addition to simultaneity of a standard of estimation system and new estimation system, lack of related regulation, accumulation of historical extensive cost data, and adjustment methodology when historical estimation data is applied to next projects. The purpose of this study is to suggest the change factors by activities for estimating historical cost for apartment housing projects. New estimation system is based on historical construction data. For application of this system, the standard adjustment methodology system is necessary. and extensive cost data should be accumulated under an unified construction work classification system. Therefore in this study, according to the construction work classification system, every apartment housing project was classified to 16 work classifications, and 7 major composed items which occupy more than 85% of construction costs are analyzed by detailed activities and by average ratio and maximum ratio each of them. In the result of the study, furniture work, foundation work and masonry work are the works which have big gap of costs between average ration and maximum ratio. In addition to suggestion of change factor by work species, 5 qualified construction specialists are interviewed and change factors in 7 major works are analyzed.

• (The)Study of the Eastern Classic
• /
• no.56
• /
• pp.117-146
• /
• 2014

A Collective intelligence as a means of creation of new knowledge is a keyword in the internet era. However, all parts of the collective intelligence is not useful. This paper examined the process of scattered historical data restoration by use of collective intelligence. and conditions for effective restoration. A restoration of historical data is the basis of research and uncovering the historical facts. In the past, some passionate researchers restored the historical data. However, its progress was slow and the amount of the restored data was not much. Moreover, the restoration itself was not easy even to verify the results. The internet made a number of researchers and the general public collaborate for restoration of historical data. Due to the collective intelligence of the internet, a possibility of historical data restoration increased. This paper examined the restoration process of the military examination roster(武科及第者) in Korean Historical Figures DB(韓國歷代人物綜合情報). The passionate users took part in the restoration in such a way to donate the old documents and inform the location of originals and articles of military examination. The results of the restoration were reliable. And this paper confirmed that passionate participants, competent and professional operators, and reliable focal point(Internet Community) should be added in order to more accurately restore the data.

• Journal of Korea Spatial Information System Society
• /
• v.10 no.4
• /
• pp.97-102
• /
• 2008

A wireless sensor network consists of many sensors that collect and transmit physical or environmental conditions at different locations to a server continuously. Many researches mainly focus on processing continuous queries on real-time data stream. However, they do not concern the problem of storing the historical data, which is mandatory to the historical queries. In this paper, we propose two time-based storage methods to store the sensor data stream and reduce the managed tuples without any loss of information, which lead to the improvement of the accuracy of query results.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2012.11a
• /
• pp.141-142
• /
• 2012

Estimating the productivity of labor has relied on the experience and intuition of an estimator, or productivity data, such as standard in practical estimation. However, when new technologies and new construction methods employed in the construction industry, Dependence of a quantity surveyor's intuition is increased. This is because of the lack of a systematic methodology which models, estimates, and measures the labor productivity, Due to the historical productivity data is unavailable. Even thought project history data contains information that is important to predict the performance of a project, It can not provide valuable information to model, estimate, and measure the labor productivity. This is because 1) the absence of the productivity measurement system with consistency, 2) the difficulty of reusability of historical data collected. Therefor, this study suggests a methodology which build a productivity model by measuring the productivity of labor and collecting the historical data systematically. In addition, this methology is applied to develop a productivity model of shop-drawing and manufacturing process using descrete event simulation.

• Journal of Information Processing Systems
• /
• v.14 no.6
• /
• pp.1385-1397
• /
• 2018

There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.

• Communications for Statistical Applications and Methods
• /
• v.13 no.3
• /
• pp.685-699
• /
• 2006

Commercial banks and other related areas have developed internal models to better quantify their financial risks. Since an appropriate credit risk model plays a very important role in the risk management at financial institutions, it needs more accurate model which forecasts the credit losses, and statistical inference on that model is required. In this paper, we propose a new method for estimating a default rate. It is a Bayesian approach using the power prior which allows for incorporating of historical data to estimate the default rate. Inference on current data could be more reliable if there exist similar data based on previous studies. Ibrahim and Chen (2000) utilize these data to characterize the power prior. It allows for incorporating of historical data to estimate the parameters in the models. We demonstrate our methodologies with a real data set regarding SOHO data and also perform a simulation study.

• Journal of Korean Society of Transportation
• /
• v.28 no.3
• /
• pp.61-74
• /
• 2010

ITS (Intelligent transportation systems) collects real-time traffic data, and accumulates vest historical data. But tremendous historical data has not been managed and employed efficiently. With the introduction of data management systems like ADMS (Archived Data Management System), the potentiality of huge historical data dramatically surfs up. However, traffic data in any data management system includes missing values in nature, and one of major obstacles in applying these data has been the missing data because it makes an entire dataset useless every so often. For these reasons, imputation techniques take a key role in data management systems. To address these limitations, this paper presents a promising imputation technique which could be mounted in data management systems and robustly generates the estimations for missing values included in historical data. The developed model, based on NPR (Non-Parametric Regression) approach, employs various traffic data patterns in historical data and is designated for practical requirements such as the minimization of parameters, computational speed, the imputation of various types of missing data, and multiple imputation. The model was tested under the conditions of various missing data types. The results showed that the model outperforms reported existing approaches in the side of prediction accuracy, and meets the computational speed required to be mounted in traffic data management systems.

• Journal of Korea Water Resources Association
• /
• v.35 no.1
• /
• pp.109-124
• /
• 2002

This study is an effort to develop a stochastic model of precipitation series that preserves the pattern of occurrence of precipitation events throughout the year as well as several characteristics of the duration, amount, and intensity of precipitation events. In this study an event cluster model is used to describe the occurrence of precipitation events. A logarithmic negative mixture distribution is used to describe event duration and separation. The number of events within each cluster is also described by the Poisson cluster process. The duration of each event within a cluster and the separation of events within a single cluster are described by a logarithmic negative mixture distribution. The stochastic model for hourly precipitation occurrence process is fitted to historical precipitation data by estimating the model parameters. To allow for seasonal variations in the precipitation process, the model parameters are estimated separately for each month. an analysis of thirty-four years of historical and simulated hourly precipitation data for Seoul indicates that the stochastic model preserves many features of historical precipitation. The seasonal variations in number of precipitation events in each month for the historical and simulated data are also approximately identical. The marginal distributions for event characteristics for the historical and simulated data were similar. The conditional distributions for event characteristics for the historical and simulated data showed in general good agreement with each other.