• 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.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.19-47
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• 2015

In this era of knowledge economy, a variety of virtual communities are proliferating for the purpose of knowledge creation and utilization. Since the voluntary contributions of members are the essential source of knowledge, member turnover can have significant implications on the survival and success of virtual communities. However, there is a dearth of research on the effect of membership turnover and even the method of measurement for membership turnover is left unclear in virtual communities. In a traditional context, membership turnover is calculated as the ratio of the number of departing members to the average number of members for a given time period. In virtual communities, while the influx of newcomers can be clearly measured, the magnitude of departure is elusive since explicit withdrawals are seldom executed. In addition, there doesn't exist a common way to determine the average number of community members who return and contribute intermittently at will. This study initially examines the limitations in applying the concept of traditional turnover to virtual communities, and proposes five membership fluidity measures based on a preliminary analysis of editing behaviors of 2,978 featured articles in English Wikipedia. Subsequently, this work investigates the relationships between three selected membership fluidity measures and group collaboration performance, reflecting a moderating effect dependent on work characteristic. We obtained the following results: First, membership turnover relates to collaboration efficiency in a right-shortened U-shaped manner, with a moderating effect from work characteristic; given the same turnover rate, the promotion likelihood for a more professional task is lower than that for a less professional task, and the likelihood difference diminishes as the turnover rate increases. Second, contribution period relates to collaboration efficiency in a left-shortened U-shaped manner, with a moderating effect from work characteristic; the marginal performance change per unit change of contribution period is greater for a less professional task. Third, the number of new participants per month relates to collaboration efficiency in a left-shortened reversed U-shaped manner, for which the moderating effect from work characteristic appears to be insignificant.

• Korean Journal of Mineralogy and Petrology
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• v.36 no.2
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• pp.147-166
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• 2023

This study aimed to develop and apply guidelines for calculating greenhouse gas emissions to activate the contribution of the Korea Institute of Geoscience and Mineral Resources (KIGAM) for institutional-level research activities. In addition, we intended to improve awareness by identifying greenhouse gas emissions from KIGAM's basic research and development (R&D) activities in fiscal 2022. Herein, the research plan and budget contents of individual projects were analyzed, whilst the boundaries and scopes of greenhouse gas emissions were determined, with 22 cases being derived as either direct, indirect, or other sources of emissions. Subsequently, research activity emissions were calculated by emission source. The greenhouse gas emissions of KIGAM's 2022 basic project R&D activities were 2,041.506 tCO2eq, of which direct emissions were 793.235 tCO2eq (38.86%), indirect emissions comprised 305.647 tCO2eq (14.97%), whilst other emissions were 942.624 tCO2eq (46.18%). In particular, greenhouse gas emissions per 100 million won in the KIGAM's basic projects for fiscal 2022 (a total of 96.661 billion won) was calculated as 2.11 tCO2eq, whilst greenhouse gas emissions per participating researcher (was 4.800 tCO2eq. Such calculations should be carried out annually rather than once and accumulated for at least 5 years. Accordingly, it will be possible to standardize specific matters that influence emissions according to differences in research field characteristics and methods, thus guiding greenhouse gas emission reduction management in the future and evaluating the contributions of Environmental, Social and Governance (ESG) management to the environmental sector.