• 제목/요약/키워드: Big Data Pattern Analysis

검색결과 172건 처리시간 0.025초

A Study on the Spatial Patterns of Tweet Data for Urban Areas by Time - A Case of Busan City - (도시 지역 트윗 데이터의 시간대별 공간분포 특성 - 부산광역시를 사례로 -)

  • Ku, Cha Yong
    • Journal of Cadastre & Land InformatiX
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    • 제46권2호
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    • pp.269-281
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    • 2016
  • The process of spatial big data, such as social media, is being paid more attention in the field of spatial information in recent years. This study, as an example of spatial big data analysis, analyzed the spatial and temporal distribution of Tweet data based on the location and time information. In addition, the characteristics of its spatial pattern by times were identified. Tweet data in Busan city are collected, processed, and analyzed to identify the characteristics of the temporal and spatial pattern. Then, the results of Tweet data analysis were compared with the characteristics of the land type. This study found that spatial pattern of tweeting in the city was associated with given time periods such as daytime and nighttime in both weekdays and weekends. The spatial distribution patterns of individual time periods were compared with the characteristics of the land for the spatially concentrated area. The results of this study showed that tweeted data would be related to different spatial distribution depending on the time, which potentially reflects the daily pattern and characteristics of the land type of urban area to some extent. This study presented the possible incorporation of social media data, e. g. Tweet data, into the field of spatial information. It is expected that there will be more advantage to use a variety of social media data in areas such as land planning and urban planning.

A Study on the Performance Degradation Pattern of Caisson-type Quay Wall Port Facilities (케이슨식 안벽 항만시설의 성능저하패턴 연구)

  • Na, Yong Hyoun;Park, Mi Yeon;Jang, Shinwoo
    • Journal of the Society of Disaster Information
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    • 제18권1호
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    • pp.146-153
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    • 2022
  • Purpose: In the case of domestic port facilities, port structures that have been in use for a long time have many problems in terms of safety performance and functionality due to the enlargement of ships, increased frequency of use, and the effects of natural disasters due to climate change. A big data analysis method was studied to develop an approximate model that can predict the aging pattern of a port facility based on the maintenance history data of the port facility. Method: In this study, member-level maintenance history data for caisson-type quay walls were collected, defined as big data, and based on the data, a predictive approximation model was derived to estimate the aging pattern and deterioration of the facility at the project level. A state-based aging pattern prediction model generated through Gaussian process (GP) and linear interpolation (SLPT) techniques was proposed, and models suitable for big data utilization were compared and proposed through validation. Result: As a result of examining the suitability of the proposed method, the SLPT method has RMSE of 0.9215 and 0.0648, and the predictive model applied with the SLPT method is considered suitable. Conclusion: Through this study, it is expected that the study of predicting performance degradation of big data-based facilities will become an important system in decision-making regarding maintenance.

Big Data Based Urban Transportation Analysis for Smart Cities - Machine Learning Based Traffic Prediction by Using Urban Environment Data - (도시 빅데이터를 활용한 스마트시티의 교통 예측 모델 - 환경 데이터와의 상관관계 기계 학습을 통한 예측 모델의 구축 및 검증 -)

  • Jang, Sun-Young;Shin, Dong-Youn
    • Journal of KIBIM
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    • 제8권3호
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    • pp.12-19
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    • 2018
  • The research aims to find implications of machine learning and urban big data as a way to construct the flexible transportation network system of smart city by responding the urban context changes. This research deals with a problem that existing a bus headway model is difficult to respond urban situations in real-time. Therefore, utilizing the urban big data and machine learning prototyping tool in weathers, traffics, and bus statues, this research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data is gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is implemented by the machine learning tool (RapidMiner Studio) and conducted several tests for bus delays prediction according to specific circumstances. As a result, possibilities of transportation system are discussed for promoting the urban efficiency and the citizens' convenience by responding to urban conditions.

A Pattern on Keyword of the Android through Utilizing Big Data Analysis (빅 데이터 분석을 활용한 스마트폰 플랫폼 키워드에 대한 패턴)

  • Jin, Chan-Yong;Nam, Soo-Tai
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 한국정보통신학회 2016년도 춘계학술대회
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    • pp.129-130
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    • 2016
  • 빅 데이터 분석은 기존 데이터베이스 관리 도구로부터 데이터를 수집, 저장, 관리, 분석할 수 있는 역량을 말한다. 대부분의 빅 데이터 분석 기술 방법들은 기존 통계학과 전산학에서 사용되던 데이터 마이닝, 기계 학습, 자연 언어 처리, 패턴 인식 등이 해당된다. 최근 스마트 기기의 발달과 정보통신기술의 발전은 트위터, 페이스북, 인스타그램 등의 소셜 네트워크상에서 유통되는 정보량이 폭발적 증가하고 있다. 이러한 변화는 데이터화가 가속화되고 있는 현대사회에서 데이터의 가치는 점점 높아질 것으로 예상되며, 데이터로부터 가치 있는 정보와 통찰력을 효과적으로 이끌어내는 기업이 경쟁력 확보를 위한 핵심가치가 되었다. 본 연구에서는 다음 커뮤니케이션의 빅 데이터 분석도구인 소셜 매트릭스를 활용하여 키워드 분석을 통해 스마트폰 플랫폼 키워드 의미를 분석하고자 한다.

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A Pattern on Keyword of the Creative Economy through Utilizing Big Data Analysis (빅 데이터 분석을 활용한 창조경제 키워드에 대한 패턴)

  • Jin, Chan-Yong;Nam, Soo-Tai
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 한국정보통신학회 2016년도 춘계학술대회
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    • pp.143-144
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    • 2016
  • 빅 데이터 분석은 기존 데이터베이스 관리 도구로부터 데이터를 수집, 저장, 관리, 분석할 수 있는 역량을 말한다. 또한, 대량의 정형 또는 비정형 데이터 집합으로부터 가치를 추출하고 결과를 분석하는 기술을 의미한다. 대부분의 빅 데이터 분석 기술 방법들은 기존 통계학과 전산학에서 사용되던 데이터 마이닝, 기계 학습, 자연 언어 처리, 패턴 인식 등이 해당된다. 글로벌 리서치 기관들은 빅 데이터를 2011년 이래로 최근 가장 주목받는 신기술로 지목해오고 있다. 따라서 대부분의 산업에서 기업들은 빅 데이터의 적용을 통해 가치 창출을 위한 노력을 기하고 있다. 본 연구에서는 다음 커뮤니케이션의 빅 데이터 분석도구인 소셜 매트릭스를 활용하여 키워드 분석을 통해 창조경제 키워드 의미를 분석하고자 한다. 또한, 분석결과를 바탕으로 이론적 실무적 시사점을 제시하고자 한다.

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A Pattern Study on Keyword of the Collagen through Utilizing Big Data Analysis (빅데이터 분석을 활용한 콜라겐 키워드에 대한 패턴)

  • Yu, Ok-Kyeong;Jin, Chan-Yong;Nam, Soo-Tai
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 한국정보통신학회 2016년도 추계학술대회
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    • pp.124-125
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    • 2016
  • 빅데이터 분석은 기존 데이터베이스 관리 도구로부터 데이터를 수집, 저장, 관리, 분석할 수 있는 역량을 말한다. 또한 대량의 정형 또는 비정형 데이터 집합으로부터 가치를 추출하고 결과를 분석하는 기술을 의미한다. 대부분의 빅데이터 분석 기술 방법들은 기존 통계학과 전산학에서 사용되던 데이터 마이닝, 기계 학습, 자연 언어 처리, 패턴 인식 등이 해당된다. 글로벌 리서치 기관들은 빅데이터를 2011년 이래로 최근 가장 주목받는 신기술로 지목해오고 있다. 따라서 대부분의 산업에서 기업들은 빅데이터의 적용을 통해 가치 창출을 위한 노력을 기울이고 있다. 본 연구에서는 다음 커뮤니케이션의 빅데이터 분석도구인 소셜 매트릭스를 활용하여 키워드 분석을 통해 콜라겐 키워드에 대한 의미를 분석하고자 한다. 또한 분석결과를 바탕으로 실무적 시사점을 제시하고자 한다.

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A study on the electric railway load pattern analysis and building database program (전기철도 부하특성 분석 및 데이터베이스 구축)

  • Jeon, Yong-Joo;Kim, Chi-Tae;Lee, Gi-Chun;Lee, Sung-Uk
    • Proceedings of the KSR Conference
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    • 한국철도학회 2006년도 추계학술대회 논문집
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    • pp.719-722
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    • 2006
  • At present, In korea one of big characteristics in electricity power market is unique seller but in the near future competitions are expected in the market. Another big trend is development of IT technology. Through IT, remote inspection for power usage are possible. So huge power consumer like KORAIL it is necessary to investigate power consumption pattern. This paper presents load consumption pattern for representative substation and billing system database program. Base on the substation annual power usage data, the characteristic of the substation power consumption are investigated and effective electrical billing system are compared each other. The database program was properly designed to examine the billings.

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Spatial Characteristics and Driving Forces of Cultivated Land Changes by Coupling Spatial Autocorrelation Model and Spatial-temporal Big Data

  • Hua, Wang;Yuxin, Zhu;Mengyu, Wang;Jiqiang, Niu;Xueye, Chen;Yang, Zhang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제15권2호
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    • pp.767-785
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    • 2021
  • With the rapid development of information technology, it is now possible to analyze the spatial patterns of cultivated land and its evolution by combining GIS, geostatistical analysis models and spatiotemporal big data for the dynamic monitoring and management of cultivated land resources. The spatial pattern of cultivated land and its evolutionary patterns in Luoyang City, China from 2009 to 2019 were analyzed using spatial autocorrelation and spatial autoregressive models on the basis of GIS technology. It was found that: (1) the area of cultivated land in Luoyang decreased then increased between 2009 and 2019, with an overall increase of 0.43% in 2019 compared to 2009, with cultivated land being dominant in the overall landscape of Luoyang; (2) cultivated land holdings in Luoyang are highly spatially autocorrelated, with the 'high-high'-type area being concentrated in the border area directly north and northeast of Luoyang, while the 'low-low'-type area is concentrated in the south and in the municipal area of Luoyang, and being heavily influenced by topography and urbanization. The expansion determined during the study period mainly took place in the Luoyang City, with most of it being transferred from the 'high-low'-type area; (3) elevation, slope and industrial output values from analysis of the bivariate spatial autocorrelation and spatial autoregressive models of the drivers all had significant effects on the amount of cultivated land holdings, with elevation having a positive effect, and slope and industrial output having a negative effect.

Tourism policy establishment plan using geographic information system and big data analysis system -Focusing on major tourist attractions in Incheon Metropolitan City- (지리정보시스템과 빅데이터 분석 시스템을 활용한 관광 정책수립 방안 -인천광역시 주요 관광지 중심으로-)

  • Min, Kyoungjun;Lim, Heuiseok
    • Journal of the Korea Convergence Society
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    • 제12권8호
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    • pp.13-21
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    • 2021
  • This study aims to analyze tourist inflow trends and consumption patterns using a geographic information system and big data analysis system. Songdo Central Park and Chinatown were selected among the major tourist destinations in Incheon, and floating population analysis and card sales analysis were conducted for one month in June 2017. The number of tourists visiting Songdo Central Park from metropolitan cities across the country was highest in the order of Incheon Metropolitan City, Gyeonggi-do, and Seoul Metropolitan City, and the proportion of foreign tourists was the highest in China. The number of card consumption used by Chinatown tourists was 12.4% higher for men than for women, and the amount of card consumption was also higher for men by 18%. This study has implications for proposing a strategic plan for tourism policy by analyzing the inflow trend and consumption pattern of tourists and deriving major issues in the establishment of tourism policy. Based on this study, it is expected that it can be helpful in improving the construction of tourism infrastructure in the future.

Determinants of Information Technology Audit Quality: Evidence from Vietnam

  • NGUYEN, Anh Huu;HA, Hanh Hong;NGUYEN, Soa La
    • The Journal of Asian Finance, Economics and Business
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    • 제7권4호
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    • pp.41-50
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    • 2020
  • The paper aims to investigate auditors, auditing firms and other external factors that affect quality of information technology audit in Vietnam. We conducted 2 types of data collections including direct and on survey. For direct survey, we sent directly to auditors at the training classes organized by State Securities Exchanges Commission. An online survey was established and Google doc link was provided to the Big4 and non-Big4 auditors. We received 138 survey responses in that 90 auditors came from Big4 and 48 auditors from non-Big4 firms. The data are analyzed using a factor analysis and compare means approaches to illustrate the potential IT audit quality factors and identify differences between two groups of auditors. The results show that independence and accounting knowledge and audit skills are the most important factors. And since external auditors perform many assurance services, the independence is critical. The result also shows that the auditors need to have enough competent and professional skills when conducting an audit, especially within an IT environment that requires high quality. The findings suggest a similar pattern of two groups in the context of Vietnam and some factors of auditors and auditing firms appear to have a statistically significant impact on quality of IT audit.