• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.712-714
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• 2004

대부분의 웹 검색 엔진들과 많은 특화된 검색 도구들은 웹 페이지의 색인화와 분석을 위한 전처리 단계로 대규모 웹 페이지들을 수집하기 위해 웹 크롤러에 의존한다. 일반적인 웹 크롤러는 몇 주 또는 몇 달의 주기에 걸쳐 수백만 개의 호스트들과 상호작용을 통해 웹 페이지 정보를 수집한다. 본 논문에서는 이러한 크롤러의 성능향상과 효율적인 실행을 위해 그리드 미들웨어인 글로버스 툴킷을 이용하여 분산된 크롤러를 제안한다. 본 웹 크롤러의 실행은 그 기능의 분산처리를 위한 각 호스트 서버들을 글로버스로 연결하고, 인증하여, 작업을 할당하는 단계와, 크롤러 프로그램이 실행되어 자료를 수집하는 단계. 마지막으로 이렇게 수집된 웹 페이지 정보들을 처음 명령한 시스템으로 반환하는 단계로 나누어진다. 결과 수집 작업을 보다 분산화 할 수 있게 하였으며 여러 대의 저 비용의 시스템에서 고 비용, 고 사양의 서버의 성능을 얻을 수 있었으며, 확장이 용이하고, 견고한 크롤러 프로그램 및 시스템 환경을 구축할 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.17-28
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• 2017

Recently, a study on data has been actively conducted because the value of the data has become more useful. Web crawler that is program of data collection recently spotlighted because it can take advantage of the various fields. Web crawler can be defined as a tool to analyze the web pages and collects the URL by traversing the web server in an automated manner. For the treatment of Big-data, distributed Web crawler is widely used which is based on the Hadoop MapReduce. But, it is difficult to use and has constraints on the performance. Apache spark that is the In-memory computing platform is an alternative to MapReduce. The search engine which is one of the main purposes of web crawler displays the information you search by keyword gathered by web crawler. If search engines implement a spark-based web crawler instead of traditional MapReduce-based web crawler, it would be a more rapid data collection.

• Journal of Digital Contents Society
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• v.18 no.5
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• pp.933-942
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• 2017

In this paper, we propose a web crawler service method for collecting information efficiently about college students and job-seeker's external activities, competition, and scholarship. The proposed web crawler service uses Jsoup tree analysis and Json format data transmission method to avoid problems of duplicated crawling while crawling at high speed. After collecting relevant information for 24 hours, we were able to confirm that the web crawler service is running with an accuracy of 100%. It is expected that the web crawler service can be applied to various web sites in the future to improve the web crawler service.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.567-581
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• 2008

As the size of the web is growing explosively, search engines are becoming increasingly important as the primary means to retrieve information from the Internet. A search engine periodically downloads web pages and stores them in the database to provide readers with up-to-date search results. The web crawler is a program that downloads and stores web pages for this purpose. A large-scale search engines uses a parallel web crawler to retrieve the collection of web pages maximizing the download rate. However, the service architecture or experimental analysis of parallel web crawlers has not been fully discussed in the literature. In this paper, we propose an architecture of the parallel web crawler and discuss implementation issues in detail. The proposed parallel web crawler is based on the coordinator/agent model using multiple machines to download web pages in parallel. The coordinator/agent model consists of multiple agent machines to collect web pages and a single coordinator machine to manage them. The parallel web crawler consists of three components: a crawling module for collecting web pages, a converting module for transforming the web pages into a database-friendly format, a ranking module for rating web pages based on their relative importance. We explain each component of the parallel web crawler and implementation methods in detail. Finally, we conduct extensive experiments to analyze the effectiveness of the parallel web crawler. The experimental results clarify the merit of our architecture in that the proposed parallel web crawler is scalable to the number of web pages to crawl and the number of machines used.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.473-476
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• 2009

본 논문에서는 다중 쓰레드 환경에서 동작하는 웹 크롤러를 구현하고 성능을 분석한다. 이 웹 크롤러의 특징은 검색시간을 단축하기 위하여 크롤링, 파싱 및 페이지랭킹, DB 저장 모듈을 서로 독립적으로 다른 작업을 수행하도록 구현한 것이다. 크롤링 모듈은 웹상의 데이터를 수집하는 기능을 제공한다. 그리고 파싱 및 페이지랭크 모듈은 수집한 데이터를 파싱하고, 웹 페이지의 상대적인 중요도를 수치로 계산하여 페이지랭크를 지정한다. DB 연동 모듈은 페이지랭크 모듈에서 구한 페이지랭크를 데이터베이스에 저장한다. 성능평가에서는 다중 쓰레드 환경에서 쓰레드 수와 웹 페이지의 수에 따른 검색 시간을 측정하여 그 결과를 비교 평가한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.227-230
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• 2015

For analyzing and prediction of economic trends, it is necessary to collect particular economic news and stock data. Typical Web crawler to analyze the page content, collects document and extracts URL automatically. On the other hand there are forms of crawler that can collect only document of a particular topic. In order to collect economic news on a particular Web site, we need to design a crawler which could directly analyze its structure and gather data from it. The wrapper-based web crawler design is required. In this paper, we design a crawler wrapper for Economic news analysis system based on big data and implemented to collect data. we collect the data which stock data, sales data from USA auto market since 2000 with wrapper-based crawler. USA and South Korea's economic news data are also collected by wrapper-based crawler. To determining the data update frequency on the site. And periodically updated. We remove duplicate data and build a structured data set for next analysis. Primary to remove the noise data, such as advertising and public relations, etc.

• The Journal of the Korea Contents Association
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• v.11 no.9
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• pp.9-16
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• 2011

There are no way of collection, preservation and utilization for web resources after the service is terminated and is gone. However, these Web resources, regardless of the importance of periodically or aperiodically updated or have been destroyed. Therefore, to collect and preserve Web resources Web archive is being emphasized. Web resources collected periodically in order to develop Web archiving crawlers only was required. In this study, from the collection of Web resources to be used for archiving existing web crawlers to analyze the strengths and weaknesses. We have developed web archiving systems for the best collection of web resources.

• Journal of Korean Society of Archives and Records Management
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• v.17 no.4
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• pp.1-26
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• 2017

This study aims to provide the efficient sharing and utilization method of disasters scattered by each institution and develop automated collection algorithm using web crawler for disaster information in deep web accounts. To achieve these goals, this study analyzes the logical structure of the deep web and develops algorithms to collect the information. With the proposed automatic algorithm, it is expected that disaster management will be helped by sharing and utilizing disaster safety information.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.317-325
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• 2018

With the aim of creating new economic values and strengthening national competitiveness, governments and major private companies around the world are continuing their interest in big data and making bold investments. In order to collect objective data, such as news, securing data integrity and quality should be a prerequisite. For researchers or practitioners who wish to make decisions or trend analyses based on objective and massive data, such as portal news, the problem of using the existing Crawler method is that data collection itself is blocked. In this study, we implemented a method of collecting web data by addressing existing crawler-style problems using the cloud service platform provided by Amazon Web Services (AWS). In addition, we collected 'gas safety' articles and analyzed issues related to gas safety. In order to ensure gas safety, the research confirmed that strategies for gas safety should be established and systematically operated based on five categories: accident/occurrence, prevention, maintenance/management, government/policy and target.