• KIISE Transactions on Computing Practices
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• v.21 no.10
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• pp.651-657
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• 2015

The amount of web traffic has increased as a result of the rapid growth of the use of web-based applications. In order to obtain valuable information from web logs, we need to develop systems that can support interactive, flexible, and efficient ways to analyze and handle large amounts of data. In this paper, we present CERES, a log-based, interactive web analytics system for backbone networks. Since CERES focuses on analyzing web log records generated from backbone networks, it is possible to perform a web analysis from the perspective of a network. CERES is designed for deployment in a server cluster using the Hadoop Distributed File System (HDFS) as the underlying storage. We transform and store web log records from backbone networks into relations and then allow users to use a SQL-like language to analyze web log records in a flexible and interactive manner. In particular, we use the data cube technique to enable the efficient statistical analysis of web log. The system provides users a web-based, multi-modal user interface.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.787-797
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• 2002

Partial residual plots, augmented partial residual plots and CERES plots are basic diagnostic tools for dealing with curvature as a function of specific predictors in regression problem. However, it is known that these plots can miss a curve or show a false curve in some cases such as predictors are related each other. Dynamic display of these plots is developed and applied. Examples demonstrate that dynamic plots are useful for obtaining additional Information on the curvature.

• Proceedings of the Zoological Society Korea Conference
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• 2001.10a
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• pp.244.1-244
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• 2001

No Abstract, See Full Text

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2012.07a
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• pp.153-156
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• 2012

• Journal of Climate Change Research
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• v.8 no.3
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• pp.187-199
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• 2017

We attempted to assess the impact of climate change on rice yields in Cambodia and to investigate adaptation strategies to climate change including more drastically shifting the planting dates and considering more fertilizer application levels. The potential yields of two wet season rice cultivars (Sen Pidao and Phka Rumduol) under two climate change scenarios in Cambodia were simulated using the CERES-Rice model. Field experiments conducted at the Cambodian Agricultural Research and Development Institute (CARDI), in 2010, 2011, and 2013 and climate variables from the HadGEM3-RA model were collected for this study. Compared with the baseline (1991-2000), yields of Sen Pidao rice will decrease under climate change and yields of Phka Rumduol rice could increase or decrease depending on fertilizer rates and the periods (2040s, 2050s, and 2080s). In general, the variations in the simulated effects of climate change on yields were more sensitive at fertilizer N100-N200 and less sensitive at fertilizer N0-N50. It is likely that forward shifts of planting date from the baseline plating date for the two cultivars in the future can be more benefitted than backward shifts. It is concluded that the CERES-Rice model can be useful to provide efficacious adaptation strategies in Cambodia.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1193-1202
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• 2018

It is important to quantitatively analyze the energy budget for understanding of long-term climate change in Arctic. High-quality and long-term radiative parameters are needed to understand the energy budget. Since most of radiative flux components based on satellite are provide for a short period, several data must be used together. It is important to acquaint differences between data to link for conjunction with several data. In this study, we investigated the comparative analysis of Arctic radiative flux product such as CERES and GEWEX to provide basic information for data linkage and analysis of changes in Arctic climate. As a result, GEWEX was underestimated the radiative variables, and it difference between the two data was about $3{\sim}25W/m^2$. In addition, the difference in high-latitude and sea ice regions have increased. In case of comparing with monthly means, the other variables except for longwave downward flux represent high difference of $9.26{\sim}26.71W/m^2$ in spring-summer season. The results of this study can be used standard data for blending and selecting GEWEX and CERES radiative flux data due to recognition of characteristics according to ice-ocean area, season, and regions.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2003.09a
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• pp.39-41
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• 2003

The crop growth simulation model could be adopted to evaluate the impact not only of the long term climate change such as atmosphere $CO_2$ concentration rising and global warming but also of the predicted short term weather variability on the national crop production. There are several growth simulation models for predicting rice crop performance such as ORYZA1, CERES-Rice, Rice Clock Model, and SIMRIW.(omitted)

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.277-283
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• 2018

The crop growth model has been widely used for climate change impact assessment. Crop growth model require genetic coefficients for simulating growth and yield. In order to determine the genetic coefficients, regional growth monitoring data or yield trial data of crops has been used to calibrate crop growth model. The aim of this study is to verify that yield trial data of corn is appropriate to calibrate genetic coefficients of CERES-MAIZE. Field experiment sites were Suwon, Jinju, Daegu and Changwon. The distance from the weather station to the experimental field were from 1.3km to 27km. Genetic coefficients calibrated by yield trial data showed good performance in silking day. The genetic coefficients associated with silking are determined only by temperature. In CERES-MAIZE model, precipitation or irrigation does not have a significant effect on phenology related genetic coefficients. Although the effective distance of the temperature could vary depending on the terrain, reliable genetic coefficients were obtained in this study even when a weather observation site was within a maximum of 27 km. Therefore, it is possible to estimate the genetic coefficients by yield trial data in study area. However, the yield-related genetic coefficients did not show good results. These results were caused by simulating the water stress without accurate information on irrigation or rainfall. The yield trial reports have not had accurate information on irrigation timing and volume. In order to obtain significant precipitation data, the distance between experimental field and weather station should be closer to that of the temperature measurement. However, the experimental fields in this study was not close enough to the weather station. Therefore, When determining the genetic coefficients of regional corn yield trial data, it may be appropriate to calibrate only genetic coefficients related to phenology.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.393-401
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• 2000

Many graphical methods have been suggested for obtaining an impression of a curvature in regression problem in which some covariates enter nonlinearly. However when true model does not belong to the class of additive models, graphical methods may contain a serious bias. A method is suggested which can avoid such bias in the fitting of nonaddive models.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.10a
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• pp.177-177
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• 2017