• Journal of the Korean Society for information Management
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• v.36 no.2
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• pp.31-56
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• 2019

The user satisfaction and needs of an academic library were caught through a questionnaire survey in this study. The aim of this study is to draw up the CRM based plan for meeting user needs on the study. The users' demographic information, library resources & their use, homepage, services, and facilities/environment of the library were categorized in the questionnaire and analyzed for this study. The major conclusions of this study are: (1) The library resources use was the highest, and its facilities/environment was the lowest in the user satisfaction study. It also revealed that there are much necessities for the quick acquisition and dissemination of the requested material to the library users, and for the inter-library loan (ILL) services among campuses for the subscription books in the library resources use study, too; (2) There are a lot of necessities for the improvement of OPAC retrieval and the subscription books system, and menu rearrangement in the library homepage; (3) There are a lot of necessities for the plans of more frequent library event, more detailed event guidance, and more reinforcement of public relations such as SMS, push services of SDI etc. in the library user services; and (4) There are a lot of necessities for the improved policies to the complaints of library users such as the lack of common study place and lockers, air conditioning and heating problem, complaints about facilities management of restroom (lack of toilet paper), library accessibility on campus, unauthorized user management etc. in the facilities/environment of the library.

• 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.

• Korean Journal of Environment and Ecology
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• v.33 no.1
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• pp.9-15
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• 2019

Artiodactyla, which is an even-toed mammal, widely inhabits worldwide. In recent years, wild Artiodactyla species have attracted public attention due to the rapid increase of crop damage and road-kill caused by wild Artiodactyla such as water deer and wild boar and the decrease of some species such as long-tailed goral and musk deer. In spite of such public attention, however, there have been few studies on Artiodactyla in Korea, and no studies have focused on the trend analysis of Artiodactyla, making it difficult to understand actual problems. Many recent studies on trend used text-mining and co-occurrence analysis to increase objectivity in the classification of research subjects by extracting keywords appearing in literature and quantifying relevance between words. In this study, we analyzed texts from research articles of three countries (Korea, China, and Japan) through text-mining and co-occurrence analysis and compared the research subjects in each country. We extracted 199 words from 665 articles related to Artiodactyla of three countries through text-mining. Three word-clusters were formed as a result of co-occurrence analysis on extracted words. We determined that cluster1 was related to "habitat condition and ecology", cluster2 was related to "disease" and cluster3 was related to "conservation genetics and molecular ecology". The results of comparing the rates of occurrence of each word clusters in each country showed that they were relatively even in China and Japan whereas Korea had a prevailing rate (69%) of cluster2 related to "disease". In the regression analysis on the number of words per year in each cluster, the number of words in both China and Japan increased evenly by year in each cluster while the rate of increase of cluster2 was five times more than the other clusters in Korea. The results indicate that Korean researches on Artiodactyla tended to focus on diseases more than those in China and Japan, and few researchers considered other subjects including habitat characteristics, behavior and molecular ecology. In order to control the damage caused by Artiodactyla and to establish a reasonable policy for the protection of endangered species, it is necessary to accumulate basic ecological data by conducting researches on wild Artiodactyla more.