• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.335-338
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• 2016

최근 디지털 정보들은 각종 매체에 저장되어 다양하게 활용되고 있다. 그 중 범죄관련 비정형데이터의 분석과 활용은 범죄수사에 유용한 자료로 활용될 수 있다. 그러나 기존의 범죄통계 자료의 분석 및 활용은 정형데이터를 이용한 제한적 접근에 그치고 있다. 따라서, 본 논문은 수사 자료 중 처리되지 못한 비정형데이터를 분석, 저장, 처리하여, 수사 자료로 활용할 수 있도록 정형데이터화 함으로 범죄 프로파일링에 도움이 될 것으로 기대된다.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.565-575
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• 2022

Thanks to the latest developments in digital architectural technologies, free-form designs that maximize the creativity of architects have rapidly increased. However, there are a lot of difficulties in forming various free-form curved surfaces. In panelizing to produce free forms, the methods of mesh, developable surface, tessellation and subdivision are applied. The process of applying such panelizing methods when producing free-form panels is complex, time-consuming and requires a vast amount of manpower when extracting production data. Therefore, algorithms are needed to quickly and systematically extract production data that are needed for panel production after a free-form building is designed. In this respect, the purpose of this study is to propose mathematical algorithms for the automatic generation of production data of free-form panels in consideration of the building model, performance of production equipment and pattern information. To accomplish this, mathematical algorithms were suggested upon panelizing, and production data for a CNC machine were extracted by mapping as free-form curved surfaces. The study's findings may contribute to improved productivity and reduced cost by realizing the automatic generation of data for production of free-form concrete panels.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.121-131
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• 2024

This research explores the potential of leveraging unstructured data from construction supervision documents, which contain detailed inspection insights from independent third-party monitors of building construction processes. With the evolution of analytical methodologies, such unstructured data has been recognized as a valuable source of information, offering diverse insights. The study introduces a framework designed to assess cost performance by applying advanced analytical methods to the unstructured data found in final construction supervision reports. Specifically, key phrases were identified using text mining and social network analysis techniques, and these phrases were then analyzed through binomial logistic regression to assess cost performance. The study found that predictions of cost performance based on unstructured data from supervision documents achieved an accuracy rate of approximately 73%. The findings of this research are anticipated to serve as a foundational resource for analyzing various forms of unstructured data generated within the construction sector in future projects.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.310-310
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• 2022

최근 통신의 발달로 인하여 웹(Web)상에는 다양한 데이터들이 실시간으로 생산되고 있으며 해당 내용은 다양한 산업에서 활용되고 있다. 특히 최근에는 재난과 관련 상황에서도 소셜 네트워크 서비스(SNS) 데이터가 활용되기도 하며 기존의 수치 계측 데이터가 아닌 하나의 센서 역할을 하는 개인의 비정형데이터의 업로드가 다양한 재난 모니터링 부분에 활용되고 있는 실정이다. 특히 홍수 등의 자연재해 발생 시 개개인의 업로드 한 웹 데이터에는 시간에 따른 인구의 유동성이나 간단한 위치 정보 등을 포함하여 실제 피해의 정도를 보다 빠르고 다양한 정보로 모니터링이 가능하다. 홍수 발생 시 일반적으로 활용하는 수문 데이터는 피해의 규모가 크게 예측되는 대하천 위주로 관측이 이루어지며 관측지역과 데이터의 양이 한정되어있어 비정형데이터를 함께 활용한 연구가 필요하다. 따라서 본 연구에서는 웹에 있는 비정형 데이터들을 추출해내는 웹 크롤러를 구성하고 해당 프로그램을 활용하여 추출한 데이터들에 대해 강우 사상과 공간적 패턴을 비교 분석하여 크라우드 소싱 데이터를 적용한 홍수 피해지도의 활용방안을 제시하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1199-1205
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• 2021

In the era of big data, not only structured data well organized in databases, but also the Internet, social network services, it is very important to effectively analyze unstructured big data such as web documents, e-mails, and social data generated in real time in mobile environment. Big data analysis is the process of creating new value by discovering meaningful new correlations, patterns, and trends in big data stored in data storage. We intend to summarize and visualize the analysis results through frequency analysis of unstructured article data using R language, a big data analysis tool. The data used in this study was analyzed for total 104 papers in the Mon-May 2021 among the journals of the Korea Institute of Information and Communication Engineering. In the final analysis results, the most frequently mentioned keyword was "Data", which ranked first 1,538 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.387-393
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• 2014

In recent years, the spread of computers is increasing, and efficient processing effort for unstructured Big Data is required. In this paper, we are proposed a system to extract the data typed in a word processor quickly by user creating and XML mapping file after converting XML data that has been entered in the office file(HWP, MS-office). In addition, we proposed a system is able to lookup the necessary data from a database by entered form in advance and convert word processor document to office files by the application program. The unstructured big data will be available to be used.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.169-170
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• 2017

In this paper, we analyze contents of formal and non - standardized data to understand what factors affect the ratings of drama. The formalized data collection collected 19 items from the four areas of drama information, person information, broadcasting information, and audience rating information of each broadcasting company. In order to collect unstructured data, crawling techniques were used to collect bulletin boards, pre - broadcast blogs and post - broadcast blogs for each drama. From the collected data, it was found that the differences according to broadcasting time, the start time, genre, and day of broadcasting were similar among broadcasting companies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1567-1574
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• 2017

In this paper, we analyzed the stereotyped and non - stereotyped data in order to analyze the drama 's ratings. The formalized data collection collected 19 items from the four areas of drama information, person information, broadcasting information, and audience rating information of each broadcasting company. Atypical data were collected from bulletin boards, pre - broadcast blogs and post - broadcast blogs operated by each broadcasting company using a crawling technique. As a result of comparing the differences according to the four areas for each broadcaster from the collected regular data, the results were similar to each other. And we derived seven related words by analyzing the correlation of occurrence frequencies from unstructured data collected from bulletin boards and blogs of each broadcasting company. The derived associations were obtained through reliability analysis.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.45-51
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• 2021

In accordance with the rapid non-face-to-face environment and mobile first strategy, the explosive increase and creation of many structured/unstructured data every year demands new decision making and services using big data in all fields. However, there have been few reference cases of using the Hadoop Ecosystem, which uses the rapidly increasing big data every year to collect and load big data into a standard platform that can be applied in a practical environment, and then store and process well-established big data in a relational database. Therefore, in this study, after collecting unstructured data searched by keywords from social network services based on Hadoop 2.0 through three virtual machine servers in the Spring Framework environment, the collected unstructured data is loaded into Hadoop Distributed File System and HBase based on the loaded unstructured data, it was designed and implemented to store standardized big data in a relational database using a morpheme analyzer. In the future, research on clustering and classification and analysis using machine learning using Hive or Mahout for deep data analysis should be continued.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.169-183
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• 2014

Massive and various types of data recorded everywhere are called big data. Therefore, it is important to analyze big data and to nd valuable information. Besides, to standardize unstructured big data is important for the application of statistical methods. In this paper, we will show how to standardize unstructured big data using MapReduce which is a distribution processing system. We also apply simple correspondence analysis and multiple correspondence analysis to nd the relationship and characteristic of direct relationship words for Samsung Electronics and The Korea Economic Daily newspaper as well as Apple Inc.