• Korean Journal of Heritage: History & Science
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• v.53 no.4
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• pp.250-273
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• 2020

The three-story stone pagodas in Hyeon-ri and Hwacheon-ri,Yeongyang Gyeongsangbuk-do are stone pagodas that exhibit the typical style of Unified Silla. The two pagodas are believed to have been built in the mid- and late 9th centuries at the latest, considering the style of the three-story roof stone on top of the double-tier base. This is also confirmed by the reliefs carved at the base and the first-story of the pagoda. The Four Heavenly Kings and the Twelve Zodiacal Animal Deities were first combined in the late 8th century in the stone pagoda at the Wonwonsa Temple Site, and the Eight Classes of Divine Beings was also the most popular carved pagoda reliefs in the 9th century. However, the two Yeongyang stone pagodas are characterized by a combination of the Four Heavenly Kings (1st story), the Eight Classes (top base), and the Twelve Zodiacal Animals (lower base), and the stone used for the pagoda consists of sedimentary rocks of the sandstone family, which comprise most of the geological strata in the Yeongyang area, rather than ordinary granite. The new combinations of the three types of guardian deities and the Eight Classes changed from seated to standing poses is interpreted as an attempt to enhance the Buddhist faith and cultural status of the Yeongyang area, along with the fact that the stone pagoda was built using local natural materials. The Eight Classes of the Yeongyang stone pagoda does not follow the two types of arrangement of the pagodas with the Eight Classes, but some of the deities have been relocated to a new location. Composed of AsuraGandharva on the east side, Naga-Mahoraga on the south, Deva-Garuda on the west, and Kimnara-Yaksa on the north, this form can be classified as a unique 'third layout of the Eight Classes' in the Yeongyang area. Such changes in the shape and posture of the reliefs reflect a new perception of the pagodas. The reason why the Gandharva and Yaksa statues were carved on the east and north sides, respectively, was because they were deemed subordinate to the Four Heavenly Kings, and the fact that the Naga and the Mahoraga were carved on the south side was presumed to have influenced the geographical location of the two pagodas on the northern side of Banbyeoncheon Stream. The Hyeon-ri and Hwacheon-ri three-story stone pagodas inherited the tradition of typical Unified Silla-period pagodas, while also bearing their own new regional characteristics.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.