• Korean Journal of Social Welfare
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• v.54
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• pp.245-272
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• 2003

Despite the importance of the program development activities and the necessity of the systematical investigation on the features of the program development activities in the social service agencies, it has been observed that recent social work studies have ignored an important study area of program development, including the activities of collecting data in the process of program development in social service agencies. Therefore, this study was undertaken to investigate salient features of the activities of collecting data for program development in the social welfare centers in Korea. A questionnaire was constructed with three parts, including a dependent variable and 6 independent variables, and 201 questionnaires were collected from 353 agencies during two months. As the result of the descriptive analyses, the five noticeable features were found, (1) the activities of collecting data for program development in the agencies are very active; (2) staff in his/her twenties are in charge of program development; (3) diverse data are collected in the process of program development (4) hard data are more collected than soft data in the process of program development; (5) the respondents are more despondent on knowledge learned from individual studies than knowledge learned from academic institutes. Multiple regressions were applied to analyze the relationship between independent variables and three kind of dependent variables - total feature of data collecting, collecting hard data, collecting soft data. The result showed that the total feature of data collecting was critically influenced by social workers' autonomy, openness, and knowledge learned from academic institutes, and workload. The activities of collecting hard data was influenced by the above variables, except social workers' workload, The activities of collecting soft data were influenced by the social workers' autonomy, openness, and knowledge learned from academic institutes, and workload. Major findings were discussed and several suggestions were made for future research and improvement of the program development in social welfare centers.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.149-158
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• 2017

Big data have been presenting us with exciting opportunities and challenges in economic development. For instance, in the agriculture sector, mixing up of various agricultural data (e.g., weather data, soil data, etc.), and subsequently analyzing these data deliver valuable and helpful information to farmers and agribusinesses. However, massive data in agriculture are generated in every minute through multiple kinds of devices and services such as sensors and agricultural web markets. It leads to the challenges of big data problem including data collection, data storage, and data analysis. Although some systems have been proposed to address this problem, they are still restricted either in the type of data, the type of storage, or the size of data they can handle. In this paper, we propose a novel design of a platform for collecting and analyzing agricultural big data. The proposed platform supports (1) multiple methods of collecting data from various data sources using Flume and MapReduce; (2) multiple choices of data storage including HDFS, HBase, and Hive; and (3) big data analysis modules with Spark and Hadoop.

• Journal of Korean Living Environment System
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• v.24 no.3
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• pp.360-367
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• 2017

Although the elderly collecting and selling wastepaper is one of the most vulnerable groups in society, this group has not been studied specifically. Subjective health status is an important aspect of quality of life in old age. The purpose of this study is to examine factors affecting subjective health status among the elderly collecting and selling wastepaper. Analyses are based on data from 2014 Korean Elderly Survey. Multiple regression analysis showed that gender, depression, and fear of falling were associated with subjective health status. The results of this study suggest that gender and mental health aspects should be considered in making intervention plan for assessments and providing services for health among the elderly collecting and selling wastepaper. The study findings also indicate that efforts to improve safety in worksite of elderly persons collecting and selling wastepaper are needed.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.411-418
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• 2007

To analyze dynamic and long-term behavior of high speed rail system, and seismic measurements been installed at the various locations in the high speed rail system. Among these, 76 measurements were installed Chunan and Deajeon, and 80 measurements between Kwangmyung and Chunan. From these 156 measurements, real time data are continuously corrected and be sent to the main monitoring system to be used further analysis. These two systems were installed by different institution, and so there are not only basic differences in the system itself, but system running methods and data collecting/transferring methodology. There has been systematic difficulties due to these differences. This study shows the whole steps of high speed rail data monitoring systems include measurement equipment itself and data collecting/transferring system. study will proposed a more effective methods for collecting. This study also discuss the real time analysis method which can be used for future high speed rail monitoring system.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.9
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• pp.259-269
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• 2017

As emerging the '4th Industrial Revolution' by increasing the necessity of the intelligent system recently, 'Autonomic Control System' also has been the important issue. It is necessary to develop the system collecting data of machines and sensors for the autonomic control system to monitor the target system. But it is difficult to collect data because data formats of machines and sensors of the existing factories differ between each manufacturer. Therefore, this paper presents and implements data collecting and processing system that comprise 3 steps including 'ParseBuffer', 'ProcessData' and 'AddToBuffer' by using 'MTConnect' that is standard manufacturing facility data collecting middleware. Through the suggested system, we can get data in a common format usable in an autonomous control system. As a case study, we experimented with the generation and collection of AGV (Automated Guided Vehicle) data, which is an unattended transportation system in the factory. To accomplish this, we defined the data type in accordance with the MTConnect standard and confirmed the data collected through the proposed system.

• The Journal of Fisheries Business Administration
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• v.36 no.3 s.69
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• pp.89-117
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• 2005

Currently, the tax - exempted vessel fuel is provided for commercial fishing in order to increase the competitive power of fishery production thorough the National Federation of Fisheries Cooperatives. The National Federation of Fisheries Cooperatives should predict the exact amount of fuel consumption for fishing every year to request the fuel from the government. Unfortunately, there is no sophisticated model to predict the tax - exempted vessel fuel consumption. In 2003, the consumption of the tax- exempted vessel fuel was only $25.1\%$ of the estimation amount by the National Federation of Fisheries Cooperatives. This causes an inefficiency in the petroleum management. Moreover, we need some data such as the annual average fishing hours, fishing days and fishing behavior to adopt a new policy regarding fishing. Up to now, the data have been obtained by survey with response in the fishery field. In the most case, we have a small number of data because we spend so much time and money consuming for collecting fishing data. As a result, the level of confidence of the data is associated with the sample size and normally low. In order to achieve more accurate data, we need to develope an efficient method for collecting fishing data. In this research, we proposed a new method to predict the tax- exempted vessel fuel consumption more exactly. The prediction results from the proposed method has been compared with the results from the current method. According to the results in this research, the method proposed here produced much better accuracy than the current method. In addition, we also proposed in the paper for collecting fishing data of the annual average fishing hours using the tax - exempted vessel fuel consumption and the gasoline consumption of vessel engine. The fishing data obtained by using the method proposed in this research could be much more efficient and accurate because it doesn't need to estimate from survey sample data.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.1
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• pp.91-99
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• 2013

In this study, a current condition of human feces-collecting and transporting business of local government was investigated using statistical data to efficiently derive proper systematic management plan for human feces-collecting and transporting business. According to the data for human feces-collecting and transporting business from 2006 to 2010, it turned out that the number of people working in this field and the amount of human feces produced and processed were decreased. They were decreased by 292 places, 1,077 employees, $904m^3/day$, and $121m^3/day$, respectively. In addition, the number of human feces-collecting and transporting business has been decreased causing an extra amount of manpower and the number of employees working in the field decreased. In this study, the reasons for financial difficulties of human feces-collecting and transporting business were analyzed. Finally the alternative plans were suggested to replace currently closed business due to management deterioration.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.664-666
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• 2014

Recently, it is issued that the big data collecting meaningless data, and then extracting meaningful data from analyzed relationship to provide various information for users. However, original data collecting methods have difficulty in collecting information Because they analyze data by using sensors installed at exterior and interior of buildings and information of users. In this paper, we develop a data collection application using smart phone to resolve this problem. Developed application transmits data about Wi-Fi and measured information such as temperature, humidity, acceleration, gyro to server through web. The data transmitted is used as a context inference for users. According to the inference result, it is possible to provide various services on user conditions.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.661-664
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• 2017

Recently, during disasters occurrence, dealing with emergencies has been handled well by the early transmission of disaster relating notifications on social media networks (e.g., Twitter or Facebook). Intuitively, with their characteristics (e.g., real-time, mobility) and big communities whose users could be regarded as volunteers, social networks are proved to be a crucial role for disasters response. However, the amount of data transmitted during disasters is an obstacle for filtering informative messages; because the messages are diversity, large and very noise. This large volume of data could be seen as Social Big Data (SBD). In this paper, we proposed a big data platform for collecting and analyzing disasters' data from SBD. Firstly, we designed a collecting module; which could rapidly extract disasters' information from the Twitter; by big data frameworks supporting streaming data on distributed system; such as Kafka and Spark. Secondly, we developed an analyzing module which learned from SBD to distinguish the useful information from the irrelevant one. Finally, we also designed a real-time visualization on the web interface for displaying the results of analysis phase. To show the viability of our platform, we conducted experiments of the collecting and analyzing phases in 10 days for both real-time and historical tweets, which were about disasters happened in South Korea. The results prove that our big data platform could be applied to disaster information based systems, by providing a huge relevant data; which can be used for inferring affected regions and victims in disaster situations, from 21.000 collected tweets.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.205-209
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• 2015

A significant amount of data including ongoing construction activities, work quantities, resources utilized by contractors, and site conditions is collected in highway construction sites on a daily basis by resident engineers. This data is commonly known as daily work reports (DWRs) in the U.S. Although a lot of time and effort is invested in collecting the DWR data, its utilization has been very limited. This paper discusses current practices of collecting and utilizing DWR data among various Departments of Transportation in the U.S., and discusses the challenges and opportunities for better collection and utilization of the data. An extensive literature review and two nationwide surveys in the U.S. were conducted as a part of this study. Finally, it provides a set of recommendations to effectively address the challenges identified and maximize the benefits of utilizing DWR data such as supporting various decisions for highway project development process. The findings of this study are implementable ideas that can aid DOTs in making data-driven decisions throughout the project development processes in the future.