• Journal of Digital Contents Society
• /
• v.18 no.5
• /
• pp.933-942
• /
• 2017

In this paper, we propose a web crawler service method for collecting information efficiently about college students and job-seeker's external activities, competition, and scholarship. The proposed web crawler service uses Jsoup tree analysis and Json format data transmission method to avoid problems of duplicated crawling while crawling at high speed. After collecting relevant information for 24 hours, we were able to confirm that the web crawler service is running with an accuracy of 100%. It is expected that the web crawler service can be applied to various web sites in the future to improve the web crawler service.

• Journal of Korea Multimedia Society
• /
• v.19 no.5
• /
• pp.808-817
• /
• 2016

Recently, the wearable computing technology with bio-sensors has been rapidly developed and utilized in various areas such as personal health, care-giving for senior citizens who live alone, and sports activities. In particular, the wearable computing equipment to measure vital signs by means of digital yarns and bio sensors is noticeable. The wearable computing devices help users monitor and manage their health in their daily lives through the customized healthcare service. In this paper, we suggest a system for monitoring and analyzing vital signs utilizing smart healthcare clothing with bio-sensors. Vital signs that can be continuously acquired from the clothing is well-known as unstructured data. The amount of data is huge, and they are perceived as the big data. Vital sings are stored by Hadoop Distributed File System(HDFS), and one can build data warehouse for analyzing them in HDFS. We provide health monitoring system based on vital sings that are acquired by biosensors in smart healthcare clothing. We implemented a big data platform which provides health monitoring service to visualize and monitor clinical information and physical activities performed by the users.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.15 no.1
• /
• pp.1-7
• /
• 2019

The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.

• Quantitative Bio-Science
• /
• v.37 no.2
• /
• pp.91-101
• /
• 2018

This study has analyzed applicable characteristics on big data of the meteorology and climate depending on press releases in the media. As a result, more than half of them were conducted by governmental departments and institutions (26.9%) and meteorological administration (25.0%). Most articles were written by journalists, especially the highest portion stems from straight articles focusing on delivering simple information. For each field, the number of cases had listed in order of rank to be exposed to the media; information service, business management, farming, livestock, and fishing industries, and disaster management, but others did rank far behind; insurance, construction, hydrology and energy. Application of big data about meteorology and climate differed depending on the seasonal change, it was directly related to temperature information during spring, to weather phenomenon such as monsoon and heat wave during summer, to meteorology and climate information during fall, and to weather phenomenon such as cold wave and heavy snow during winter.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.117-119
• /
• 2016

Various sites which provide big data analysis service do not show the interpretation of analysis results such as social trends and events but simple numeric results. In this point, this study attempts to suggest a way of interpretation on big data analysis results focusing on brand awareness and preference.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2014.07a
• /
• pp.117-118
• /
• 2014

최근 공공기관의 공공데이터 제공이 활성화 되고 있으며, 이를 활용한 응용서비스에 대한 요구도 증가하고 있는 추세이다. 현재 교통정보예측 플랫폼은 실시간 교통정보 또는 과거 교통정보이력을 분석하여 미래의 교통량이나 도착시간정보를 제공하고 있으나 날씨, 사고 등과 같은 미래 교통정보에 즉각적인 영향을 줄 수 있는 요소를 배제하고 있어 높은 신뢰도를 확보하기 어렵다. 본 논문에서는 교통정보예측에 영향을 주는 요소인 기상, 사고, 교통정보와 같은 공공데이터를 효율적으로 수집 저장 처리할 수 있는 저장방식 및 신뢰도 높은 교통정보를 예측할 수 있는 예측기술이 포함된 b-Traffic 서비스 플랫폼을 제시한다.

• KNOM Review
• /
• v.22 no.2
• /
• pp.12-21
• /
• 2019

There is a rapid development of experimental equipment and ICT technology in data-intensive scientific areas, thus, big data of more than exabyte size is being generated. However, the big data transmission technology does not satisfy the needs of the application researchers who utilize it. Various high-performance transmission technologies have been developed based on QoS(Quality of Service), but they also require changes in the clean slate method. On the other hand, ScienceDMZ technologies improve the performance of scientific big data transmission by bypassing the firewall that causes a big problem in transmission performance. In addition, it is possible to implement without changing the existing network. In this paper, we built ScienceDMZ in an international long-distance environment based on KREONET(Korea Research Environment Open NETwork), and we verified the performance. We also introduced how GPU platform could be linked in a distributed ScienceDMZ environment.

• Journal of Internet Computing and Services
• /
• v.15 no.1
• /
• pp.55-61
• /
• 2014

Various human-friendly robot services have been developed and mobile cloud computing is a real time computing service that allows users to rent IT resources what they want over the internet and has become the new-generation computing paradigm of information society. The enterprises and nations are actively underway of the business process using mobile cloud computing and they are aware of need for implementing mobile cloud computing to their business practice, but it has some week points such as authentication services and distributed processing technologies of big data. Sometimes it is difficult to clarify the objective of cloud computing service. In this study, the vulnerability of authentication services on mobile cloud computing is analyzed and mobile cloud computing model is constructed for efficient and safe business process. We will also be able to study how to process and analyze unstructured data in parallel to this model, so that in the future, providing customized information for individuals may be possible using unstructured data.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.15 no.4
• /
• pp.44-53
• /
• 2016

The era of Big Data has come and the importance of Big Data has been rapidly growing. The part of transportation, the Four-Step Travel Demand Model(FSTDM), a traditional Trip-Based Model(TBM) reaches its limit. In recent years, a traffic demand forecasting method using the Activity-Based Model(ABM) emerged as a new paradigm. Given that transportation means the spatial movement of people and goods in a certain period of time, transportation could be very closely associated with spatial data. So, I mined Spatial Big Data from SNS. After that, I analyzed the character of these data from SNS and test the reliability of the data through compared with the attributes of TBM. Finally, I built a database from SNS for the operation of ABM and manipulate an ABM simulator, then I consider the result. Through this research, I was successfully able to create a spatial database from SNS and I found possibilities to overcome technical limitations on using Spatial Big Data in the transportation planning process. Moreover, it was an opportunity to seek ways of further research development.

• Health Policy and Management
• /
• v.32 no.2
• /
• pp.154-163
• /
• 2022

Background: The costliness index (CI) is an index that is used in various ways to improve the quality of medical care and the management of appropriate treatment in medical institutions. However, the current calculation method for CI has a limitation in reflecting the actual medical cost of the patient unit because the outpatient and inpatient costs are evaluated separately. It is desirable to calculate the CI by integrating the medical cost into the episode unit. Methods: We developed an episode-based CI method using the episode classification system of the Centers for Medicare and Medicaid Services to the National Inpatient Sample data in Korea, which can integrate the admission and ambulatory care cost to episode unit. Additionally, we compared our new method with the previous method. Results: In some episodes, the correlation between previous and episode-based CI was low, and the proportion of outpatient treatment costs in total cost and readmission rates are high. As a result of regression analysis, it is possible that the level of total medical costs of the patient unit in low volume medical institute and rural area has been underestimated. Conclusion: High proportion of outpatient treatment cost in total medical cost means that some medical institutions may have provided medical services in the ambulatory care that are ancillary to inpatient treatment. In addition, a high readmission rate indicates insufficient treatment service for inpatients, which means that previous CI may not accurately reflect actual patient-based treatment costs. Therefore, an integrated patient-unit classification system which can be used as a more effective CI indicator is needed.