• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.79-98
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• 2009

Web traffic is becoming an important indicator to make inferences about internet companies' future prospects so that traditional firm valuation methods need to be modified to integrate the ideas of web traffic information as a major asset of internet companies. It is because web traffic is a measure of attracting visitors to firm's web site and is the basis for internet companies' marketing expenditure and customer acquisition and retention. Also the web traffic represents the internet companies' technological advances and marketability. The major purpose of this study is to show the relevance of web traffic for valuation of internet companies. For this, we test hypothesis with the firm's web traffic and financial data using the analysis model of Hand(2000a) derived from the log-linear model introduced by Ye and Finn(1999). Test results show that the web traffic, more specifically the number of unique visitors, visits, and page views are all positively related to the firm's value. This implies that the web traffic information should be considered as one of the important non-financial indicator for the internet firm valuation.

• 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 KIISE:Information Networking
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• v.37 no.5
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• pp.317-326
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• 2010

Nowadays, transmission bandwidth for network traffic is increasing and the type is varied such as peer-to-peer (PZP), real-time video, and so on, because distributed computing environment is spread and various network-based applications are developed. However, as PZP traffic occupies much volume among Internet backbone traffics, transmission bandwidth and quality of service(QoS) of other network applications such as web, ftp, and real-time video cannot be guaranteed. In previous research, the port-based technique which checks well-known port number and the Deep Packet Inspection(DPI) technique which checks the payload of packets were suggested for solving the problem of the P2P traffics, however there were difficulties to apply those methods to detection of P2P traffics because P2P applications are not used well-known port number and payload of packets may be encrypted. A proposed algorithm for identifying P2P heavy traffics based on flow transport parameters and behavioral characteristics can solve the problem of the port-based technique and the DPI technique. The focus of this paper is to identify P2P heavy traffic flows rather than all P2P traffics. P2P traffics are consist of two steps i)searching the opposite peer which have some contents ii) downloading the contents from one or more peers. We define P2P flow patterns on these P2P applications' features and then implement the system to classify P2P heavy traffics.

• Journal of the Korean Society for information Management
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• v.36 no.3
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• pp.149-174
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• 2019

This study analyzes the actual use data of the websites of university libraries, analyzes the users' usage behavior, and proposes improvement measures for the websites. The study analyzed users' traffic and analyzed their usage behavior from January 2018 to December 2018 on the C University website. The website's analysis tool used 'Google Analytics'. The web traffic variables were analyzed in five categories: user general characteristics, user environment analysis, visit analysis, inflow analysis, site analysis, and site analysis based on the metrics of sessions, users, page views, pages per session, average session time, and bounce rate. As a result, 1) In the analysis results of general characteristics of users, there was some access to the website not only in Korea but also in China. 2) In the user experience analysis, the main browser type appeared as Internet Explorer. The next place was Chrome, with a bounce rate of Safari, third and fourth, double that of the Explore or Chrome. In terms of screen resolution, 1920x1080 resolution accounted for the largest percentage, with access in a variety of other environments. 3) Direct inflow was the highest in the inflow media analysis. 4) The site analysis showed the most page views out of 4,534,084 pages, followed by the main page, followed by the lending/extension/history/booking page, the academic DB page, and the collection page.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11
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• pp.3479-3489
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• 2000

Most of the study on fairness control method for Distributed-Queue Dual-Bus(DQDB) have been performed under specific load types such as equal probability load types or symmetric load types. On Web-based internet enviroments client-server load types are more practical traffic patlerns than specrfic load types. In this paper, an effiective fairness control method to distribute DQDR network bandwidth fairly to all stations under a client-server load is proposed. In order to implement a dynamic bandwidth timing capabihty needed to distribute the bandwidth fairty at heavy loads, the proposed method uses two pararnetexs, one is an access hrnit to legulate each station's packet transmission and the other is the number of extra emply slots that are yielded to downstream stations. In point of implementation this mechanism is simpler and easier than Bandwidth Tuning Mechanism(BTM) that uses an intermediate pattern and an adptation function. Simulation results show that it outperforms othen mecharusms.

• Journal of the Korean Society for information Management
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• v.38 no.3
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• pp.141-174
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• 2021

In this study, by examining the actual usage data of the university library website before and after COVID-19 outbreak, the usage behavior of users was analyzed, and the data before and after the virus outbreak was compared, so that university libraries can provide more efficient information services in a pandemic situation. We would like to suggest ways to improve it. In this study, the user traffic made on the website of University C was 'using Google Analytics', from January 2018 to December 2018 before the oneself of the COVID-19 virus and from January 2020 to 2020 after the outbreak of the virus. A comparative analysis was conducted until December. Web traffic variables were analyzed by classifying them into three characteristics: 'User information', 'Path', and 'Site behavior' based on metrics such as session, user, number of pageviews, number of pages per session time, and bounce rate. To summarize the study results, first, when compared with data from January 1 to January 20 before the oneself of COVID-19, users, new visitors, and sessions all increased compared to the previous year, and the number of sessions per user, number of pageviews, and number of pages per session, which showed an upward trend before the virus outbreak in 2020, increased significantly. Second, as social distancing was upgraded to the second stage, there was also a change in the use of university library websites. In 2020 and 2018, when the number os students was the lowest, the number of page views increased by 100,000 more in 2020 compared to 2018, and the number of pages per session also recorded10.46, which was about 2 more pages compared to 2018. The bounce rate also recorded 14.38 in 2018 and 2019, but decreased by 1 percentage point to 13.05 in 2020, which led to more active use of the website at a time when social distancing was raised.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3088-3096
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• 1997

In this paper, we present WWW system over native ATM services. The use of native ATM services through ATM API can provide better performance and functionality than that of IP over ATM, LAN Emulation or Multiprotocol over ATM. Our WWW browser and server provide advanced WWW services based on enhanced performance and guaranteed QoS support by using native ATM service benefits. This paper describes and compares advantages and disadvantages of Native ATM Services and ATM Internet Services, and addresses ATM API standardization and development trend that are made by the ATNI Forum for the support of native ATM services, and then describes the architecture and operation of our WWW browser and server using ATM API. The system architecture is based on HTTP over ATM API capable of supporting guaranteed QoS over its connections. The system defines and uses new HTML attributes within hyperlinking HTML elements for the description of ATM QoS and traffic characteristics that are derived from UNI signaling 3.1 connection characteristics information elements. Our system uses WinSock 2 API as its ATM API.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.