• Publications of The Korean Astronomical Society
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• v.10 no.1
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• pp.135-146
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• 1995

We have developed a graphic software for image processing of astronomical data obtained by observational equipment in Astronomical Observatory of Kyung Hee University. The essential hardware for running our computer program is simply composed of a PC with the graphic card to handle 256 colors and the color graphic monitor, including CCD camera system. Our software has been programmed in WINDOWS to provide good environments for users, by using various techniques of image processing on astronomical image data recorded in FITS format by KHCCD program(Jin and Kim, 1994) with a compressional mode. We are convinced that our results will be a fundamental and useful technique in the construction of data processing system and can be effectively used in any other observatories, as well as in data processing system of Kyung Hee University.

• Publications of The Korean Astronomical Society
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• v.21 no.1
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• pp.1-9
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• 2006

An archiving system designed to enable documenting database of astronomical images, with functions of search and download, is being developed by Korean Astronomical Data Center(KADC) of Korea Astronomy and Space Science Institute(KASI). The system consists of three PCs for web server, database server, and system management server. The search program for the web environment is operated in the web server. In the management server, several utility program we developed are installed: input program for the database, program for transfer from fits to jpg files, program for data recovery and management, and programs for statistics and connect management. The collected data would be sorted out by the system manager to input into the database. The online input is possible in an observatory where the data is produced. We applied the content management system(CMS) module for the database management. On the basic of CMS module, we set up a management system for the whole life cycle of metadata from creation and collection to storage and deletion of the data. For the search function, we employed a technique to extract indices from the metadata. In addition, MySQL is adopted for DBMS. We currently display about 2,700 and 25,000 photographs for astronomical phenomena and astronomical objects on the data, respectively.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.88.3-89
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• 2017

We present our experiences with open-source NoSQL database systems in analyzing spatial and temporal astronomical data. We conduct experiments of using Redis in-memory NoSQL database system by modifying and exploiting its support of geohash for astronmical spatial data. Our experiment focuses on performance, cost, difficulty, and scalability of the database system. We also test OpenTSDB as a possible NoSQL database system to process astronomical time-series data. Our experiments include ingesting, indexing, and querying millions or billions of astronomical time-series measurements. We choose our KMTNet data and the public VVV (VISTA Variables in the Via Lactea) catalogs as test data. We discuss issues in using these NoSQL database systems in astronomy.

• Publications of The Korean Astronomical Society
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• v.23 no.2
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• pp.53-63
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• 2008

Korea Astronomy and Space Science Institute (KASI), direct decendant of Korea National Astronomy Observatory, has been publishing Korean Astronomical Almanac since in 1976. The almanac contains essential data in our daily lives such as the times of sunrise, sunset, moonrise, and moonset, conversion tables between luni-solar and solar calendars, and so forth. So, we are planning to register Korean astronomical almanac data for national Standard Reference Data(SRD), which is a scientific/technical data whose the reliablity and the accuracy are authorized by scientific analysis and evalution. To be certificated as national SRD, reference data has to satisfy several criteria such as traceability, consistency, uncertainty, and so on. Based on similarity among calculation processes, we classified astronomical almanac data into three groups: Class I, II, and III. We are planning to register them for national SRD in consecutive order. In this study, we analyzed Class I data which is aimed to register in 2009, and presented the results. Firstly, we found that the traceability and the consistency can be ensured by the usage of NASA/JPL DE405 ephemeris and by the comparsion with international data, respectively. To evaluate uncertainty in Class I data, we solved the mathematical model and determined the factors influencing the calculations. As a result, we found that the atmospheric refraction is the main factor and leads to a variation of ${\pm}16$ seconds in the times of sunrise and sunset. We also briefly review the histories of astronomical almanac data and of standard reference data in Korea.

• Publications of The Korean Astronomical Society
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• v.19 no.1
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• pp.121-128
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• 2004

We have constructed a database of Korean historical astronomical records. The database contains observational data recorded from BC 2183 to AD 1910. We have also built a webpage for searching through the database based on the following criteria: (1) dynasties (2) astronomical phenomena (3) reigning kings (4) references (5) keywords. Users may select two or more dynasties to search through the database for a certain phenomena, and compare data with those of other dynasties. The queried data can be primarily sorted by one critetion, and secondarily sorted by another, each in ascending or descending order. The search results give dates both in Solar and Lunisolar calendars, years and dates in Sexagenary cycle, dynasties, reigning kings, astronomical phenomena, and references. The database and webpage were constructed under the research project of the Korean Astronomical Data Center (KADC, http://kadc.kao.re.kr) in Korea Astronomy Observatory (KAO).

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1107-1116
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• 2016

Recent astronomical survey observations have produced substantial amounts of data as well as completely changed conventional methods of analyzing astronomical data. Both classical statistical inference and modern machine learning methods have been used in every step of data analysis that range from data calibration to inferences of physical models. We are seeing the growing popularity of using machine learning methods in classical problems of astronomical data analysis due to low-cost data acquisition using cheap large-scale detectors and fast computer networks that enable us to share large volumes of data. It is common to consider the effects of inhomogeneous spatial and temporal coverage in the analysis of big astronomical data. The growing size of the data requires us to use parallel distributed computing environments as well as machine learning algorithms. Distributed data analysis systems have not been adopted widely for the general analysis of massive astronomical data. Gathering adequate training data is expensive in observation and learning data are generally collected from multiple data sources in astronomy; therefore, semi-supervised and ensemble machine learning methods will become important for the analysis of big astronomical data.

• Publications of The Korean Astronomical Society
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• v.18 no.1
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• pp.43-49
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• 2003

Korean Astronomical Data Center (KADC, http://kadc.kao.re.kr) in Korea Astronomy Observatory (KAO) has constructed an archive of Bohyunsan Optical Astronomy Observatory (BOAO) 1.8m telescope data. The archive is consisted of photometric (1KCCD, 2KCCD) and spectroscopic data of 400GB amount for the period of 1997 to 2002,and the first web service is made of the data from 1997 to 2001. In the search page, primary search criterion of object name or coordinates is used. Users can also refine the search criteria using parameters such as observation date, observer(s), data type, and/or instrument. The data identified from the search can be uploaded to the FTP site for further downloading in FITS format. This archive is the first DB of astronomical data made in Korea.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.31.1-31.1
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• 2011

We present wide-field near-IR imaging polarimetry of 30 Doradus in the Large Magellanic Cloud, using the InfraRed Survey Facility (IRSF). We obtained polarimetry data in J, H, and Ks bands using the JHKs-simultaneous imaging polarimeter SIRPOL. Since many Galactic field stars along the line-of-sight to the Large Magellanic Cloud are contaminated in our data, we developed methods to identify the foreground sources using the proper motion data. We investigated polarimetric properties between the Galactic foreground stars and the stars in the LMC.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.279-281
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• 2015

We adopt the PASTEL catalog combined with SIMBAD radial velocities as a testing standard to validate the stellar parameters (effective temperature $T_{eff}$, surface gravity log g, metallicity [Fe/H] and radial velocity $V_r$) from the first data release (DR1) of The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) survey. After applying data reduction and temperature constraints to the sample obtained by cross-identification, we compare the stellar parameters from DR1 and PASTEL. The results show that the DR1 results are reliable under certain conditions. We derive a dispersion of 110 K, 0.19 dex, 0.11 dex and $4.91kms^{-1}$ in specified effective temperature ranges, for $T_{eff}$, log g, [Fe/H] and $V_r$ respectively. Systematic errors are negligible except for those of $V_r$. In addition, for stars with PASTEL [Fe/H] < -1:5, the metallicities in DR1 are systematically higher than those in PASTEL.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.63.1-63.1
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• 2018

The modern large-scale surveys and state-of-the-art cosmological simulations produce various kinds of big data composed of millions and billions of galaxies. Inevitably, we need to adopt modern Big Data platforms to properly handle such large-scale data sets. In my talk, I will briefly introduce the de facto standard of modern Big Data platform, Apache Spark, and present some examples to demonstrate how Apache Spark can be utilized for solving data-driven astronomical problems.