• 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.

• Horticultural Science & Technology
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• v.34 no.6
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• pp.886-897
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• 2016

The physical properties of rooting media for the establishment of plugs in a greenhouse are modified according to variations in the greenhouse environment throughout the season. In this study, we established a standard for rooting media for the production of plug seedlings for each growing season (summer, winter and spring fall). Eight types of peatmoss (PM) and 4 types of perlite (PL) commonly used in Korea were collected and blended with the ratio of 7 parts PM to 3 parts PL (v/v) to make 32 different rooting media blends. We determined the total porosity (TP), container capacity (CC), air-filled porosity (AFP), pH, and electrical conductivity (EC) of the 32 media blends, and 6 media blends were selected for seasonal use. We also conducted additional analyses for plant easily available water (EAW), buffering water (BW), cation exchange capacity (CEC), and nutrient contents in the 6 media blends. The TP, CC, and AFP of the 32 media blends ranged from 64.7 to 96.0%, 42.9 to 90.1%, and 1.3 to 27.8%, respectively, indicating that the physical properties were strongly influenced by the type of PM and PL. The pH and EC of the PMs ranged from 2.96 to 3.81 and 0.08 to $0.47dS{\cdot}m^{-1}$, respectively. However, after blending the PM with the PL the pH was raised and the EC was lowered The media blends selected for the summer growing season were Blonde Golden peatmoss (BG) + No. 1 perlite size < 1 mm (PE1) and Latagro 0-10 mm (L1) + No. 2 perlite size 1-2 mm (PE2). These two media blends had 89.8-90.9% of TP, 80.8-81.3% of CC, and 9.0-9.7% of AFP. The media blends selected for the winter growing season were Sfagnumi Turvas (ST) + PE2 and Latagro 20-40 mm (L3) + PE2. These media blends had 79.9-86.7% of TP, 60.4-74.9% of CC, and 11.8-19.6% of AFP. The TP, CC, and AFP of two media blends, BG + No.3 perlite 2-5 mm (PE3) and Orange peatmoss (O) + PE3, selected for the spring and fall growing seasons, respectively, were 85.2-87.3%, 77.9%, and 7.4-9.4%, respectively. The percentage of EAW of the media blends selected for the spring, summer, and winter growing seasons ranged from 24.2-24.9%, 22.0-28.6%, and 18.0-21.8%, respectively, but the percentages of BW were not significantly different among the selected root media blends. The pH, EC, and CEC of the 6 selected media blends ranged from 3.11-3.97, $0.06-0.26dS{\cdot}m^{-1}$, and $97-119meq{\cdot}100g^{-1}$, respectively.