• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.187-189
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• 2001

In this study, the Web Camera System is implementation and simulated on two different architectures. In the one architecture, a Web-server and Camera-server are implemented on the same system, and the system transfers motion picture which compressed to JPEG file to users on the WWW(World Wide Web). In the other architecture, the Web-server and Camera-server are implemented on different systems, and the motion picture is transferred from the Camera-server to Web-server, and finally to users. In order to compare system performance between two architecture, data traffic is measured and simulated in the unit of byte per second and frame per second.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.3
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• pp.416-420
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• 2002

Traditional queueing analyses are very useful for designing a network's capacity and predicting there performances, however most of the predicted results from the queueing analyses are quite different from the realistic measured performance. And recent empirical studies on LAN, WAN and VBR traffic characteristics have indicated that the models used in the traditional Poisson assumption can't properly predict the real traffic properties due to under estimation of the long range dependence of network traffic and self-similarity In this parer self-similar characteristics over statistical approaches and real time network traffic measurements are estimated It is also shown that the self- similar traffic reflects network traffic characteristics by comparing source model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.104-107
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• 2002

Traditional queueing analyses are very useful for designing a network's capacity and predicting there performances, however most of the predicted results from the queueing analyses are quite different from the realistic measured performance. And recent empirical studies on LAN, WAN and VBR traffic characteristics have indicated that the models used in the traditional Poisson assumption can't properly predict the real traffic properties due to under estimation of the long range dependence of network traffic and self-similarity. In this paper self-similar characteristics over statistical approaches and real time network traffic measurements are estimated. It is also shown that the self-similar traffic reflects network traffic characteristics by comparing source model.

• 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 Korean Society of Transportation
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• v.34 no.1
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• pp.1-14
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• 2016

In the recent years, the internet has become accessible without limitation of time and location to anyone with smartphones. It resulted in more convenient travel information access both on the pre-trip and en-route phase. The main objective of this study is to conduct a stationary test for traffic information web/mobile application access indexes from TCS (Toll Collection System); and analyzing the relationship between the web/mobile application access indexes and actual traffic volume on expressways, in order to analyze searching behavior of expressway related travel information. The key findings of this study are as follows: first, the results of ADF-test and PP-test confirm that the web/mobile application access indexes by time periods satisfy stationary conditions even without log or differential transformation. Second, the Pearson correlation test showed that there is a strong and positive correlation between the web/mobile application access indexes and expressway entry and exit traffic volume. In contrast, truck entry traffic volume from TCS has no significant correlation with the web/mobile application access indexes. Third, the time gap relationship between time-series variables (i.e., concurrent, leading and lagging) was analyzed by cross-correlation tests. The results indicated that the mobile application access leads web access, and the number of mobile application execution is concurrent with all web access indexes. Lastly, there was no web/mobile application access indexes leading expressway entry traffic volumes on expressways, and the highest correlation was observed between webpage view/visitor/new visitor/repeat visitor/application execution counts and expressway entry volume with a lag of one hour. It is expected that specific individual travel behavior can be predicted such as route conversion time and ratio if the data are subdivided by time periods and areas and utilizing traffic information users' location.

• Journal of Advanced Navigation Technology
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• v.13 no.5
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• pp.669-676
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• 2009

HTTP or audio/video streaming services are good candidates for future centralized navigation system and in order to provide stability for such services, service providers use a cluster of web servers. In this paper, we provide the criteria for web server cluster design of vehicular users with consideration of differentiated access per different user classes. Several feasible scenarios are examined and their performance analysis using queueing theory is presented to provide the foundation for web server cluster design using traffic load balancer. Through the thorough analysis, efficient criteria for traffic load balancer design is derived. In order to satisfy users' service requirements, priority services controlled by traffic load balancer are considered and analyzed. We also provide the evaluation of the accuracy of the analytical model through simulation.

• 한국ITS학회:학술대회논문집
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• v.2007 no.10
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• pp.303-307
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• 2007

• IEMEK Journal of Embedded Systems and Applications
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• v.1 no.2
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• pp.64-72
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• 2006

The long propagation delay over satellite internet causes degradation of TCP performance in slow start phase. Especially, web traffic performance is greatly reduced by low throughput in slow start phase. To improve web traffic performance, we propose the Dynamic ACK Generation Scheme which generates ACKs and considers sender RTO in PEP (Performance Enhancing Proxy). The Normal ACK generation mechanism improves TCP throughput, and also decreases sender RTO. if PEP stops generating ACKs, TCP performance will be reduced by frequent RTO expiration. To solve this problem, our scheme adjusts RTO using ACK generation interval. And it supports retransmission mechanism for loss recovery in PEP. The results of the performance analysis provide a good evidence to demonstrate the efficiency of our mechanisms over satellite internet.

• The KIPS Transactions:PartC
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• v.9C no.4
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• pp.605-614
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• 2002

Amount of the web traffic web server handles are explosively increasing, which requires that the performance of the web server should be improved for the various web services. Although the analysis for the HTTP traffic with the proper tuning for the web server is essential, the research relevant to the subject are insignificant. In particular, although most of applications are implemented over HTTP 1.1 protocol, the researches mostly deal with the performance evaluation of the HTTP 1.0 protocol. Consequently, the modeling approach and the performance evaluation over HTTP 1.1 protocol have not been well formed. Therefore, basing on the HTTP 1.1 protocol supporting persistent connection, we present an analytical end-to-end tandem queueing model for web server to consider the specific hardware configuration inside web server beginning at accepting the user request until completing the service. we compare various performances between HTTP 1.0 and HTTP 1.1 under the overloading condition, and then analyze the characteristics of the HTTP traffic that include file size requested to web server, the OFF time between file transfers, the frequency of requests, and the temporal locality of requests. Presented model is verified through the comparing the server throughput according to varying requests rate with the real web server. Thereafter, we analyze the performance evaluation of the web server, according to the interrelation between TCP Listen queue size, the number of HTTP threads and the size of the network buffers.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.