EMACrawler: Web Search Engine Database Freshness Optimization

Zülfü Alanoğlu; M. Ali Akcayol

Article Details

EMACrawler: Web Search Engine Database Freshness Optimization

Authors:

Zülfü Alanoğlu | M. Ali Akcayol

Publisher:

Gazi University

Main Topics:

Bilgisayar Yazılımı

Journal:

Politeknik Dergisi Vol.27 Issue 6, pp.2201-2214

Document Type:

Article

Publication Date:

12/2024

Document Language:

Turkish

Document ID (Org No.):

2001333510

pISSN:

2147-9429

Keywords:

Abstract:

 In todays information and technology age, search engines have become an important part of our lives. Although search engines are the first to be used to access information, old and unnecessary information is included in the content offered to users. In terms of providing up-to-date data, todays search engines often cannot offer the desired success. In order to keep the data presented by web browsers up-to-date, the time of return visits must be accurately estimated. In this study, EMACrawler based on exponential moving average is proposed to determine the revisit times, which is the most important feature that affects the performance of search engines. The proposed method is tested using precision, total coverage and efficiency metrics. It has been seen that EMACrawler obtains the current data on the web pages in an accurate and quick manner. As a result of the experimental studies, it has been seen that EMACrawler is more successful than other methods in obtaining up-to-date data and maintaining the freshness of the browser database.

 In todays information and technology age, search engines have become an important part of our lives. Although search engines are the first to be used to access information, old and unnecessary information is included in the content offered to users. In terms of providing up-to-date data, todays search engines often cannot offer the desired success. In order to keep the data presented by web browsers up-to-date, the time of return visits must be accurately estimated. In this study, EMACrawler based on exponential moving average is proposed to determine the revisit times, which is the most important feature that affects the performance of search engines. The proposed method is tested using precision, total coverage and efficiency metrics. It has been seen that EMACrawler obtains the current data on the web pages in an accurate and quick manner. As a result of the experimental studies, it has been seen that EMACrawler is more successful than other methods in obtaining up-to-date data and maintaining the freshness of the browser database.

 In todays information and technology age, search engines have become an important part of our lives. Although search engines are the first to be used to access information, old and unnecessary information is included in the content offered to users. In terms of providing up-to-date data, todays search engines often cannot offer the desired success. In order to keep the data presented by web browsers up-to-date, the time of return visits must be accurately estimated. In this study, EMACrawler based on exponential moving average is proposed to determine the revisit times, which is the most important feature that affects the performance of search engines. The proposed method is tested using precision, total coverage and efficiency metrics. It has been seen that EMACrawler obtains the current data on the web pages in an accurate and quick manner. As a result of the experimental studies, it has been seen that EMACrawler is more successful than other methods in obtaining up-to-date data and maintaining the freshness of the browser database.

 Günümüz bilgi ve teknoloji çağında arama motorları hayatımızın önemli bir parçası haline gelmiştir. Her ne kadar bilgiye erişimde ilk başvurulan arama motorları olsa da kullanıcılara sunulan içerikte eski ve gereksiz bilgiler yer almaktadır. Güncel verileri sağlamak açısından günümüzdeki arama motorları çoğunlukla istenen başarıyı sunamamaktadır. Web tarayıcılarının sunduğu verilerin güncelliğini sağlamak için tekrar ziyaret zamanının doğru tahmin edilmesi gerekmektedir. Bu çalışmada arama motorlarının performanslarını etkileyen en önemli özellik olan tekrar ziyaret zamanlarının belirlenmesi için üstel hareketli ortalamaya dayanan EMACrawler önerilmiştir. Önerilen yöntem kesinlik, toplam kapsama alanı ve verimlilik metrikleri kullanılarak test edilmiştir. EMACrawlerın web sayfalarındaki güncel veriyi doğru tahmin zamanında ve hızlı bir şekilde elde ettiği görülmüştür. Yapılan deneysel çalışmaların sonucunda EMACrawlerın güncel verilerin elde edilmesi ve tarayıcı veri tabanının tazeliğinin korunmasında diğer yöntemlerden daha başarılı olduğu görülmüştür.

Article Details

EMACrawler: Web Search Engine Database Freshness Optimization

Authors:

Zülfü Alanoğlu | M. Ali Akcayol

Publisher:

Gazi University

Main Topics:

Bilgisayar Yazılımı

Journal:

Politeknik Dergisi Vol.27 Issue 6, pp.2201-2214

Document Type:

Article

Publication Date:

12/2024

Document Language:

Turkish

Document ID (Org No.):

2001333510

pISSN:

2147-9429

Web crawler

update module

data collection

data indexingWeb tarayıcısı

güncelleme modülü

veri toplama

veri indeksleme

Issues

Article Details

Authors: Zülfü Alanoğlu | M. Ali Akcayol

Publisher: Gazi University

Main Topics: Bilgisayar Yazılımı

Journal: Politeknik Dergisi Vol.27 Issue 6, pp.2201-2214

Document Type: Article

Publication Date: 12/2024

Document Language: Turkish

Document ID (Org No.): 2001333510

pISSN: 2147-9429

Issues

Authors:

Zülfü Alanoğlu | M. Ali Akcayol

Publisher:

Gazi University

Main Topics:

Bilgisayar Yazılımı

Journal:

Politeknik Dergisi Vol.27 Issue 6, pp.2201-2214

Document Type:

Article

Publication Date:

12/2024

Document Language:

Turkish

Document ID (Org No.):

2001333510

pISSN:

2147-9429