• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.395-398
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• 2017

We propose a web crawler system with keyword extraction function in this paper. Researches on the keyword extraction in existing text mining are mostly based on databases which have already been grabbed by documents or corpora, but the purpose of this paper is to establish a real-time keyword extraction system which can extract the keywords of the corresponding text and store them into the database together while grasping the text of the web page. In this paper, we design and implement a crawler combining RAKE keyword extraction algorithm. It can extract keywords from the corresponding content while grasping the content of web page. As a result, the performance of the RAKE algorithm is improved by increasing the weight of the important features (such as the noun appearing in the title). The experimental results show that this method is superior to the existing method and it can extract keywords satisfactorily.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.1-6
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• 2022

Real-time systems using web cralwing data must provide users with data from the same database as remote data. To do this, the web crawler repeatedly sends HTTP(HtypeText Transfer Protocol) requests to the remote server to see if the remote data has changed. This process causes network load on the crawling server and remote server, causing problems such as excessive traffic generation. To solve this problem, in this paper, based on user events, we propose a real-time web crawling technique that can reduce the overload of the network while securing the reliability of maintaining the sameness between the data of the crawling server and data from multiple remote locations. The proposed method performs a crawling process based on an event that requests unit data and list data. The results show that the proposed method can reduce the overhead of network traffic in existing web crawlers and secure data reliability. In the future, research on the convergence of event-based crawling and time-based crawling is required.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.575-584
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• 2013

Data types used for big-data analysis are very widely, such as news, blog, SNS, papers, patents, sensed data, and etc. Particularly, the utilization of web documents offering reliable data in real time is increasing gradually. And web crawlers that collect web documents automatically have grown in importance because big-data is being used in many different fields and web data are growing exponentially every year. However, existing web crawlers can't collect whole web documents in a web site because existing web crawlers collect web documents with only URLs included in web documents collected in some web sites. Also, existing web crawlers can collect web documents collected by other web crawlers already because information about web documents collected in each web crawler isn't efficiently managed between web crawlers. Therefore, this paper proposed a distributed web crawler. To resolve the problems of existing web crawler, the proposed web crawler collects web documents by RSS of each web site and Google search API. And the web crawler provides fast crawling performance by a client-server model based on RMI and NIO that minimize network traffic. Furthermore, the web crawler extracts core content from a web document by a keyword similarity comparison on tags included in a web documents. Finally, to verify the superiority of our web crawler, we compare our web crawler with existing web crawlers in various experiments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.1-7
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• 2023

Deep web collection means entering a query in a search form and collecting response results. It is estimated that the information possessed by the deep web has about 450 to 550 times more information than the statically constructed surface web. The static method does not show the changed information until the web page is refreshed, but the dynamic web page method updates the necessary information in real time and provides real-time information without reloading the web page, but crawler has difficulty accessing the updated information. Therefore, there is a need for a way to automatically collect information on these deep webs using a crawler. Therefore, this paper proposes a method of utilizing scripts as general links, and for this purpose, an algorithm that can utilize client scripts like regular URLs is proposed and experimented. The proposed algorithm focused on collecting web information by menu navigation and script execution instead of the usual method of entering data into search forms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.1-9
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• 2012

It is well known that internet shoppers are very sensitive to sale prices. They visit the various shopping malls and collect the product information including purchase conditions for goods purchase decision-making. Recently the necessity of information support is increasing because of increase of information amount which is necessary and complexity of goods purchase decision-making process. The comparison shopping agent systems have provided price comparison information which is collected from various shopping malls to satisfy internet shoppers information craving. But the frequent price change caused by keen price competition is becoming the primary reason of information quality decline among price comparison sites. RSS which is a family of web feed formats used to publish frequently updated is applied even in on-line shopping malls. This paper develops a RSS product information collection system to get real-time product information. The proposed product information system consists of (1) web crawler module for searching RSS feed shopping malls automatically, (2) RSS reader module for parsing product information from RSS feed file, (3) product DB and (4) product searching module. Performance of the proposed system is higher than the comparison shopping agent systems when it is defined with the volume of collecting product information per unit time.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.261-269
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• 2017

The purpose of this paper is to provide a search system using a method of accessing the web in real time without using a database server in order to guarantee the up-to-date information in a single network, rather than using a plurality of bots connected by a wide area network Design. The method of the research is to design and analyze the system which can search the person and keyword quickly and accurately in crepe system. In the crepe server, when the user registers information, the body tag matching conversion process stores all the information as it is, since various styles are applied to each user, such as a font, a font size, and a color. The crepe server does not cause a problem of body tag matching. However, when executing the crepe retrieval system, the style and characteristics of users can not be formalized. This problem can be solved by using the html_img_parser function and the Go language html parser package. By applying queues and multiple threads to a general-purpose web crawler, rather than a web crawler design that targets a specific site, it is possible to utilize a multiplier that quickly and efficiently searches and collects various web sites in various applications.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.5
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• pp.61-76
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• 2018

Everything is coming into the world of network where modern people are living. The Internet of Things that attach sensors to objects allows real-time data transfer to and from the network. Mobile devices, essential for modern humans, play an important role in keeping all traces of everyday life in real time. Through the social network services, information acquisition activities and communication activities are left in a huge network in real time. From the business point of view, customer needs analysis begins with SNS data. In this research, we want to build an automatic collection system of SNS contents of web environment in real time using Python. We want to help customers' needs analysis through the typical data collection system of Instagram, Twitter, and YouTube, which has a large number of users worldwide. It is stored in database through the exploitation process and NLP process by using the virtual web browser in the Python web server environment. According to the results of this study, we want to conduct service through the site, the desired data is automatically collected by the search function and the netizen's response can be confirmed in real time. Through time series data analysis. Also, since the search was performed within 5 seconds of the execution result, the advantage of the proposed algorithm is confirmed.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.259-264
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• 2021

Recently, as the number of used trading sites supporting used trading increases, users want to search for a variety of information in real time. This new change has enabled a new type of C2C (Commerce to Commerce) transaction in the e-commerce base. However, since each used trading site has its own characteristics, it is difficult to standardize the whole. Therefore, in this paper, we studied a system that provides the transaction data used by the user in real time and provides the desired information quickly. In this paper, we researched the crawler system necessary for the development of the integrated trading system for used goods through Internet e-commerce, and made it possible to provide information in the web environment desired by the user through the defined morpheme analyzer. Therefore, in this study, we designed a system that provides information desired by users without accessing various used goods sites.

• 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.