• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.14-28
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• 2015

Numerous research has been conducted using internet search data since the mid-2000s. For example, Google Inc. developed a service predicting influenza patterns using the internet search data. The main objective of this study is to prove the hypothesis that highway traffic indices are similar to the internet search patterns. In order to achieve this objective, a model to predict the number of vehicles entering the expressway and space-mean speed was developed and the goodness-of-fit of the model was assessed. The results revealed several findings. First, it was shown that the Google search traffic was a good predictor for the TCS entering traffic volume model at sites with frequent commute trips, and it had a negative correlation with the TCS entering traffic volume. Second, the Naver search traffic was utilized for the TCS entering traffic volume model at sites with numerous recreational trips, and it was positively correlated with the TCS entering traffic volume. Third, it was uncovered that the VDS speed had a negative relationship with the search traffic on the time series diagram. Lastly, it was concluded that the transfer function noise time series model showed the better goodness-of-fit compared to the other time series model. It is expected that "Big Data" from the internet search data can be extensively applied in the transportation field if the sources of search traffic, time difference and aggregation units are explored in the follow-up studies.

• Journal of Korea Technology Innovation Society
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• v.16 no.4
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• pp.1109-1133
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• 2013

In an effort to discover new technologies and to forecast social changes of technologies, a number of technology life-cycle models have been developed and employed. The hype cycle, a graphical tool developed by a consulting firm, Gartner, is one of the most widely used models for the purpose and it is recognised as a practical one. However, more research is needed on theoretical frames, relations and empirical practices of the model. In this study, hype cycle comparisons in Korean and global search websites were performed by means of web-search traffic which is proposed as an empirical measurement of public expectation, analysed in a specific product or country in previous researches. First, search traffic and market share for new cars were compared in Korea and the U.S. with a view to identifying differences between the hype cycles in the two countries about the same product. The results show the similarity between the two countries with the statistical significance. Next, comparative analysis between search traffic and supply rate for several products in Korea was conducted to check out their patterns. According to the analysis, all the products seem to be at the "Peak of inflated expectations" in the hype cycles and they are similar to one another in the hype cycle. This study is of significance in aspects of expanding the scope of hype cycle analysis with web-search traffic because it introduced domestic web-search traffic analysis from Naver to analyse consumers' expectations in Korea by comparison with that from Google in other countries. In addition, this research can help to explain social phenomina more persuasively with search traffic and to give scientific objectivity to the hype cycle model. Furthermore, it can contribute to developing strategies of companies, such as marketing strategy.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2013.10a
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• pp.518-531
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• 2013

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.327-330
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• 2002

웹 환경에서 효율적인 인터넷 서비스를 위한 동적 서비스의 다양한 요구 사항들을 만족시키고자 많은 연구들이 시도되고 있다. 그러나, 한정적인 네트웍 대역폭으로 인한 네트웍 트래픽 증가 및 서버 시스템의 부하로 안정적인 정보 서비스가 이루지지 않고 있는 실정이며, 또한 기존의 정보 서비스 형태에 있어 직접적인 사용자에 의한 정보 검색의 형태로만 정보를 서비스 받음으로써 새로운 형태의 정보 서비스 지원방식이 요구된다. 따라서, 본 논문에서는 분산환경에서 효율적인 정보 검색 과 안정적인 정보 서비스, 네트웍 트래픽 감소를 지원하는 CORBA 기반의 멀티 에이전트 모델을 설계한다. 이는 각 개별 에이전트들이 상호 보완적 관계를 유지하여 에이전트 간 상호 작용을 통해 네트웍 트래픽 감지를 통한 안정적이고 능동적인 정보 서비스, 검색 시간 및 네트웍 트래픽 감소, 검색 키워드 유지를 통한 정확한 정보 검색 서비스, 시스템 자원의 자동 관리 등을 지원함으로써 사용자에 대한 정보 서비스의 질을 향상시킬 수 있다.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.4
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• pp.100-110
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• 1999

The decision method for the selection of search engine and the number of returned documents for meta search engine proposed in this paper could provide a solution to reduce network traffic and to maintain the precision ratio. The experiments are performed to evaluate the proposed scheme using currently popular search engines and most frequently used queries.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.05a
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• pp.549-558
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• 2017

바야흐로 4차 산업혁명의 시대가 도래하면서, 4차 산업혁명과 관련된 기술에 대한 관심 또한 나날이 높아져 가고 있다. 이런 시대적 상황에 맞춰, 본 연구에서는 웹 검색트래픽을 활용하여 4차 산업혁명 관련 기술의 국내 외 트렌드를 비교하고 관련된 기술에 대한 국내 정부R&D 과제 현황에 대해 분석해 보고자 한다. 본 분석은 먼저 관련 기술에 대한 정부 R&D 현황 과제 현황을 네트워크 분석하고 분석된 기술을 4차 산업혁명의 3가지 카테고리로 구분한다. 이후 카테고리별로 선별된 각각의 기술에 대해 구글의 웹 검색트래픽을 활용하여 관련된 기술의 국내 외 트렌드를 비교 분석한다. 본 연구를 통해 4차 산업혁명 관련 기술에 대한 과거와 현재의 관심 동향을 확인할 수 있을 것으로 예상된다. 또한, 해당 기술과 관련된 정부 R&D 과제 현황을 네트워크 분석하고 시각화하여 4차 산업혁명 관련 R&D 현황에 대한 직관적인 정보를 제공하고자 한다. 본 연구는 인류 최대의 혁명으로 정의되는 4차 산업혁명 기술에 대한 국내 외 트렌드 정보와 함께 현재까지 진행되어온 정부의 R&D 현황을 직관적으로 볼 수 있게 제공함으로써, 4차 산업혁명과 함께 다가오는 미래에 대한 올바른 정책 방향 설정에 기여할 것으로 기대된다.

• Electronics and Telecommunications Trends
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• v.30 no.4
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• pp.181-190
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• 2015

요즘 3.20 대란이나 한수원 침해사고 같은 각종 사이버 공격이 빈번하게 발생하고 있다. 이러한 사이버 공격에 대응하기 위하여 네트워크 트래픽을 수집 저장하고 사이버 공격분석에 사용하려는 다양한 노력이 행해지고 있다. 그러나 일반적으로 10Gbps 같은 고속 네트워크에서 네트워크 트래픽을 수집 저장하는 것은 쉬운 일이 아니다. 그래서 이 문건에서는 10Gbps 네트워크 트래픽 수집 저장에 관한 기술동향을 다루고 이어서 수집된 대용량 트래픽을 효율적으로 검색하기 위하여 비트맵 인덱스를 생성하는 다양한 방법을 소개하고 마지막으로 비트맵 인덱스를 사용한 효율적인 검색방법에 대해 소개한다.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2012.11a
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• pp.19-28
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• 2012

• Journal of the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.69-78
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• 2005

As the study and use of P2P systems are diversified, the effect of excessive amount of traffic, which occurs in searching peers' resource and is considered as a network bandwidth Problem, cannot let the matter Pass without making a protest. In case P2P application doesn't reduce network traffic, it can be much effected to use bandwidth smoothly in the internet environment where various network applications lie scattered and there will be inconvenience when many network users makes use of related applications . In this Paper, we propose a pure P2P model based-broadcasting technique for producing successful hit ratio and traffic amount in the weakly connected environment based-P2P system where situation of peers' connection and exit is ambiguous . The proposed searching technique is designed/implemented to improve a resident problem in the related system and we have estimated the performance of the proposed searching technique comparing our technique with the existing broadcasting based-searching technique .