• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• Asia pacific journal of information systems
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• v.23 no.1
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• pp.87-106
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• 2013

In the service-oriented economy, knowledge and skills are considered core resources to secure competitive advantages and service innovation. Knowledge management capability, which facilitates to produce, share, accumulate and reuse knowledge, becomes as important as knowledge itself to create service value. Along with knowledge management capability, dynamic capability and operational capability are the key capabilities related to managing service delivery processes. Previous studies indicated that these three capabilities are related to service innovation. Although separately investigate the relationship between the three capabilities. The purpose of this study is 1) to define variables that have effects on service innovation including knowledge management capability, dynamic capability and operational capability, and 2) to empirically test to identify relationship among variables. In this study, knowledge management capability is defined as the capability to manage knowledge process. Dynamic capability is regarded as the firm's ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments. Operational capability refers to a high-level routine that, together with its implementing input flows, confers upon an organization's management a set of decision options for producing significant outputs of a particular type. The proposed research model was tested against the data collected through the survey method. The survey questionnaire was distributed to the managers who participated in an educational program for management consulting. Each individual who answered the questionnaire represented a knowledge based service firm. About 212 surveys questionnaires were sent via e-mail or directly delivered to respondents. The number of useable responses was 93. Measurement items were adapted from previous studies to reflect the characteristics of the industry each informant worked in. All measurement items were in, 5 point Likert scale with anchors ranging from strongly disagree (1) to strongly agree (5). Out of 93 respondents, about 81% were male, 82% of respondents were in their 30s. In terms of jobs, managers were 39.78%, professions/technicians were 24.73%, researchers were 12.90%, and sales people were 10.75%. Most of respondents worked for medium size enterprises (47,31%) in their, less than 30 employees (46.24%) in their number of employees, and less than 10 million USD (65.59%) in terms of sales volume. To test the proposed research model, structural equation modeling (SEM) technique (SPSS 16.0 and AMOS version 5) was used. We found that the three organizational capabilities have influence on service innovation directly or indirectly. Knowledge management capability directly affects dynamic capability and service innovation but indirectly affect operational capability through dynamic capability. Dynamic capability has no direct impact on service innovation, but influence service innovation indirectly through operational capability. Operational capability was found to positively affect service innovation. In sum, three organizational capabilities (knowledge management capability, dynamic capability and operational capability) need to be strategically managed at firm level, because organizational capabilities are significantly related to service innovation. An interesting result is that dynamic capability has a positive effect on service innovation only indirectly through operational capability. This result indicates that service innovation might have a characteristics similar to process innovation rather than product orientation. The results also show that organizational capabilities are inter-correlated to influence each other. Dynamic capability enables effective resource management, arrangement, and integration. Through these dynamic capability affected activities, strategic agility and responsibility get strength. Knowledge management capability intensify dynamic capability and service innovation. Knowledge management capability is the basis of dynamic capability as well. The theoretical and practical implications are discussed further in the conclusion section.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.

• Korean Journal of Environment and Ecology
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• v.31 no.4
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• pp.381-396
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• 2017

Understanding species distribution plays an important role in conservation as well as evolutionary biology. In this study, we applied a species distribution model to predict hotspot areas and habitat characteristics for endangered herpetofauna species in South Korea: the Korean Crevice Salamander (Karsenia koreana), Suweon-tree frog (Hyla suweonensis), Gold-spotted pond frog (Pelophylax chosenicus), Narrow-mouthed toad (Kaloula borealis), Korean ratsnake (Elaphe schrenckii), Mongolian racerunner (Eremias argus), Reeve's turtle (Mauremys reevesii) and Soft-shelled turtle (Pelodiscus sinensis). The Kori salamander (Hynobius yangi) and Black-headed snake (Sibynophis chinensis) were excluded from the analysis due to insufficient sample size. The results showed that the altitude was the most important environmental variable for their distribution, and the altitude at which these species were distributed correlated with the climate of that region. The predicted distribution area derived from the species distribution modelling adequately reflected the observation site used in this study as well as those reported in preceding studies. The average AUC value of the eigh species was relatively high ($0.845{\pm}0.08$), while the average omission rate value was relatively low ($0.087{\pm}0.01$). Therefore, the species overlaying model created for the endangered species is considered successful. When merging the distribution models, it was shown that five species shared their habitats in the coastal areas of Gyeonggi-do and Chungcheongnam-do, which are the western regions of the Korean Peninsula. Therefore, we suggest that protection should be a high priority in these area, and our overall results may serve as essential and fundamental data for the conservation of endangered amphibian and reptiles in Korea.

• Culinary science and hospitality research
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• v.9 no.4
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• pp.163-178
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• 2003

The purpose of this study is to find the effect of restaurant brand image upon service quality, price acceptability, and revisit intention, and to propose the importance of brand image to operators and managers who manage restaurants. To accomplish the purpose of this study, sampling was taken among customers who visit six deluxe hotels and six family restaurants in Seoul. Six hundreds questionnaires were distributed to each hotel and restaurant and 487 valid samples were selected for statistical analysis. The questionnaire consists of 77 items about demographical characteristics, brand image, service quality, revisit intention, price acceptability, and spending patterns. SPSS WIN 10.0 was used for statistical analysis. A research model was built up and three null hypotheses were established. Based on theses research model and three null hypotheses, the test was conducted, and the results are as follows. Brand image has an effect upon service quality, and furthermore this can be preceding variable of service quality. Also Service quality has an effect upon price acceptability and revisit intention.

• Journal of Life Science
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• v.24 no.10
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• pp.1151-1156
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• 2014

Traditional medicinal plants are widely used to treat many diseases, such as inflammation, infections, and even cancer. Ulmus macrocarpa Hance, a Chinese elm species, is distributed in Korea, China, and Japan. The stem bark is widely employed in Korean traditional medicine to treat dermatitis, mastitis, and edema. The aim of this study was to investigate whether water extract of U. macrocarpa Hance bark (Ulmus cortex) has a immune-modulating function in a mouse model. Three different concentrations (30 mg/kg, 100 mg/kg, and 300 mg/kg) of Ulmus cortex water extract (UCWE) were orally administered to mice for 14 days, and their immune responses were analyzed. Cytokines, such as interleukin (IL)-2, IL-12, and IFN-${\gamma}$, increased in the blood of UCWE-fed groups when compared with a control group. In contrast, the IL-4 level did not change in any of the UCWE-fed groups Cell-mediated cytotoxicity was also assayed using lymphokine-activated killer cells (LAK). LAK showed greater cytotoxicity in the UCWE-fed groups than LAK in the control group. Internal organ indices, such as liver, kidney, spleen, and thymus, were similar in all the groups, including the control group, indicating that UCWE may have been nontoxic in the experimental animals. These data suggest that UCWE has an immune-modulating function in a mouse model.

• Journal of Environmental Policy
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• v.9 no.2
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• pp.157-184
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• 2010

Global climate change is destroying the water circulation balance by changing rates of precipitation, recharge and discharge, and evapotranspiration. The Intergovernmental Panel on Climate Change (IPCC 2007) makes "changes in rainfall pattern due to climate system changes and consequent shortage of available water resource" a high priority as the weakest part among the effects of human environment caused by future climate changes. Groundwater, which occupies a considerable portion of the world's water resources, is related to climate change via surface water such as rivers, lakes, and marshes, and "direct" interactions, being indirectly affected through recharge. Therefore, in order to quantify the effects of climate change on groundwater resources, it is necessary to not only predict the main variables of climate change but to also accurately predict the underground rainfall recharge quantity. In this paper, the authors selected a relevant climate change scenario, In this context, the authors selected A1B from the Special Report on Emission Scenario (SRES) which is distributed at Korea Meteorological Administration. By using data on temperature, rainfall, soil, and land use, the groundwater recharge rate for the research area was estimated by period and embodied as geographic information system (GIS). In order to calculate the groundwater recharge quantity, Visual HELP3 was used as main model for groundwater recharge, and the physical properties of weather, temperature, and soil layers were used as main input data. General changes to water circulation due to climate change have already been predicted. In order to systematically solve problems associated with how the groundwater resource circulation system should be reflected in future policies pertaining to groundwater resources, it may be urgent to recalculate the groundwater recharge quantity and consequent quantity for using via prediction of climate change in Korea in the future and then reflection of the results. The space-time calculation of changes to the groundwater recharge quantity in the study area may serve as a foundation to present additional measures for the improved management of domestic groundwater resources.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.422-432
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• 2018

This study is designed to find out the causal relation between social support, career capability, career decision-making self-efficacy and career adaptability for the career interrupted women. For this purposes the scales of social support, career capability, career decision related self efficacy and career adaptability were used. For this study, the questionnaires were distributed from Mar. 2, 2018 to Apr. 6, 2018 to 960 to the career interrupted women who had the experience of getting training at the vocational school, beauty school and women's centers located in Seoul and Gyeonggi area (composed of married women in their 30s and 50s who were receiving the education of beauty care for more than 1-9 months). 920 questionnaires got responded from the women and they were used for the final analysis with Cronbach's ${\alpha}$, exploratory factor analysis, factor analysis, frequency analysis and correlation analysis performed with SPSS program and the structural equation performed with AMOS program. The findings from the analysis are as follows; First, it was found that the structural model between career capability, social support, career decision related self efficacy and career adaptability are proper. Second, the path coefficient of the structural model was found to be statistically significant with respect to all of career capability, social support, career decision related self efficacy and career adaptability. Third, it was found that in the relation between career capability, social support and career adaptability, the career decision related self efficacy has the mediating effect. These results show that if the level of career adaptability is to be enhanced for the caber interrupted women, the career decision related self efficacy can make the critical role in addition to the career capability and social support. So, this study tries to offer the basic data required for the preparation of career and the development of future career for the success of career interrupted women going back to the workplace.

• Journal of the Korean Society for Marine Environment & Energy
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• v.7 no.1
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• pp.1-12
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• 2004

The summer distribution of $Cha-{alpha}$ and physical processes for simulating outbreak region of red tide were estimated by the Eco-Hydrodynamic model in Chinhae Bay. As a result of simulation of surface residual currents, the southward flow come in contact with the northward flow at the inlet and western part of bay in case of windlessness and below wind velocity 2 m/sec. As wind velocity increases, the velocity and direction of currents were fairly shifted. The predicted concentration of $Cha-{alpha}$ exceeded 20 mg/㎥ in Masan and Haengam Bays, and most regions were over 10 mg/㎥, which meant the possibility of red tide outbreak. From the results of the contributed physical processes to $Cha-{alpha}$, accumulation sites were distributed at the northern part of Kadok channel, around the Chilcheon island, the western part of Kajo island and some area of Chindong Bay. On the other hand, inner parts of the study area such as Masan Bay were estimated as the sites of strong algal activities. Masan and Haengam Bay are considered as the initial outbreak region of red tide by the modeling and observed data, and then red tide expanded to other areas such as physical accumulation region and western inner bay, as depending on environmental variation. The increase of wind velocity led to decrease of $Cha-{alpha}$ and enlargement of accumulation region. The variation of intensity of radiation and sunshine duration caused to rapidly fluctuation of $Cha-{alpha}$: however, it was not largely affected by the variation of pollutant loads from the land only.