Web Tarayıcıları için Etkili Tohum URL Seçimi ve Kapsam Genişletme Algoritması

Web, hızla büyüyen ve her türden verilerin bulunduğu devasa bir veri kaynağıdır. Kullanıcılar bu veri kaynağından istedikleri verileri almak için arama motorlarını kullanırlar. Arama motorları bu verileri web tarayıcıları ile elde ederler. Web tarayıcıları web sayfalarındaki tek düzen kaynak bulucuları (URL-Uniform Resource Locator) izleyerek ulaştıkları tüm sayfalardaki verileri alır, ayrıştırır ve indekslerler. Web tarama sürecindeki en önemli konular hangi URL’lerden başlanacağı ve taramanın kapsamıdır. Bu yazıda kapsamı tüm web olan genel bir tarayıcının tohum URL seçim ve kapsam genişletme yöntemleri sunulmuştur. Tohum URL seçiminde 102 farklı ülkede ziyaretçinin günlük harcadığı saat, ziyaretçi başına günlük sayfa görüntüleme sayısı, aramadan gelen trafiğin yüzdesi ve toplam bağlı site sayısı temel alınarak oluşturulmuş üç farklı tohum URL seti oluşturulup detaylı bir şekilde performansları analiz edilmiştir. Ayrıca kapsamı hızlı bir şekilde genişletmek için link skoruna dayalı yeni bir tarama algoritması önerilmiş, tohum URL setleri kullanılarak taramalar yapılmış, karşılaştırılmış ve detaylı analizleri yapılmıştır.

Anahtar Kelimeler:

Web Tarayıcıları, Tohum URL Seçimi, Kapsam Genişletme, Link Skoru Hesaplama

Effective Seed URL Selection and Scope Extension Algorithm for Web Crawler

The web is a huge data source which is rapidly growing and which keeps all kinds of data. Users use search engines to get the data they want from this data source. Search engines obtain these data through web crawlers. Web crawlers retrieve, parse, and index data on all pages they reach by tracking uniform resource locators (URL) on web pages. The most important issues in the web crawling process are which URLs to start from, and the scope of the crawl. In this study, seed URL selection and scope expansion methods of a general web crawler were presented. In the selection of seed URLs, three different seed URL sets were created based on the daily hours spent by the visitors in 102 different countries, the number of daily page views per visitor, the percentage of traffic from the search, and the total number of affiliate sites, and their performance was analyzed thoroughly. Furthermore, a new search algorithm based on link score was proposed to expand the scope quickly, searches were made, compared, and detailed analyzes were performed using seed URL sets.

Keywords:

Web crawler, Seed URL Selection, Scope extension, Link score calculation,

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[1] "Internet Users Distribution in the World." https://www.internetworldstats.com/stats.htm (accessed 30/03/2022, 2022).
[2] M. Abu Kausar, V. Dhaka, and S. Singh, "Web Crawler: A Review," International Journal of Computer Applications, vol. 63, pp. 31-36, 02/01 2013, doi: 10.5120/10440-5125.
[3] S. M. Pavalam, S. V. K. Raja, F. K. Akorli, and M. Jawahar, "A survey of web crawler algorithms," International Journal of Computer Science Issues (IJCSI), vol. 8, no. 6, p. 309, 2011.
[4] F. Menczer, G. Pant, P. Srinivasan, and M. E. Ruiz, "Evaluating topic-driven Web crawlers," in Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, 2001, pp. 241-249.
[5] A. Arasu, J. Cho, H. Garcia-Molina, A. Paepcke, and S. Raghavan, "Searching the web," ACM Transactions on Internet Technology (TOIT), vol. 1, no. 1, pp. 2-43, 2001.
[6] C. Castillo, "Effective web crawling," SIGIR Forum, vol. 39, no. 1, pp. 55–56, 2005, doi: 10.1145/1067268.1067287.
[7] X. Zhang and K. P. Chow, "A Framework for Dark Web Threat Intelligence Analysis," International Journal of Digital Crime and Forensics (IJDCF), vol. 10, no. 4, pp. 108-117, 2018, doi: 10.4018/IJDCF.2018100108.
[8] M. R. Henzinger, "Algorithmic challenges in web search engines," Internet Mathematics, vol. 1, no. 1, pp. 115-123, 2004.
[9] S. Daneshpajouh, M. M. Nasiri, and M. Ghodsi, "A Fast Community Based Algorithm for Generating Web Crawler Seeds Set," in WEBIST (2), 2008, pp. 98-105.
[10] J. M. Kleinberg, "Authoritative sources in a hyperlinked environment," 1998, vol. 98: Citeseer, pp. 668-677.
[11] S. Zheng, P. Dmitriev, and C. L. Giles, "Graph-based seed selection for web-scale crawlers," presented at the Proceedings of the 18th ACM conference on Information and knowledge management, Hong Kong, China, 2009. [Online]. Available: https://doi.org/10.1145/1645953.1646277.
[12] B. Ganguly and R. Sheikh, "A review of focused web crawling strategies," International Journal of Advanced Computer Research, vol. 2, no. 4, p. 261, 2012.
[13] F. J. M. Shamrat, Z. Tasnim, A. S. Rahman, N. I. Nobel, and S. A. Hossain, "An effective implementation of web crawling technology to retrieve data from the world wide web (www)," International Journal of Scientific & Technology Research, vol. 9, no. 01, pp. 1252-1256, 2020.
[14] L. Jiang and H. Zhang, "Multi-agent based individual web spider system," in 2010 World Automation Congress, 2010: IEEE, pp. 177-181.
[15] S.-B. Chan and H. Yamana, "The method of improving the specific language focused crawler," in CIPS-SIGHAN Joint Conference on Chinese Language Processing, 2010.
[16] F. Menczer and A. E. Monge, "Scalable web search by adaptive online agents: An infospiders case study," in Intelligent Information Agents: Springer, 1999, pp. 323-347.
[17] P. N. Priyatam, A. Dubey, K. Perumal, S. Praneeth, D. Kakadia, and V. Varma, "Seed selection for domain-specific search," presented at the Proceedings of the 23rd International Conference on World Wide Web, Seoul, Korea, 2014. [Online]. Available: https://doi.org/10.1145/2567948.2579216.
[18] L. Sanagavarapu, S. Sarangi, R. Reddy, and V. Varma, Fine Grained Approach for Domain Specific Seed URL Extraction. 2018.
[19] L. M. Sanagavarapu, S. Sarangi, and Y. R. Reddy, "ABC Algorithm for URL Extraction," in ICWE Workshops, 2017.
[20] S. Pavalam, S. K. Raja, F. K. Akorli, and M. Jawahar, "A survey of web crawler algorithms," International Journal of Computer Science Issues (IJCSI), vol. 8, no. 6, p. 309, 2011.
[21] N. Alderratia and M. Elsheh, "Using Web Pages Dynamicity to Prioritise Web Crawling," in Proceedings of the 2019 2nd International Conference on Machine Learning and Machine Intelligence, 2019, pp. 40-44.
[22] J. Cho, H. Garcia-Molina, and L. Page, "Efficient crawling through URL ordering," Computer networks and ISDN systems, vol. 30, no. 1-7, pp. 161-172, 1998.
[23] J. Prakash and R. Kumar, "Web Crawling through Shark-Search using PageRank," Procedia Computer Science, vol. 48, pp. 210-216, 2015/01/01/ 2015, doi: https://doi.org/10.1016/j.procs.2015.04.172.
[24] L. Cao et al., "Rankcompete: Simultaneous ranking and clustering of information networks," Neurocomputing, vol. 95, pp. 98-104, 2012.
[25] M. Najork and J. L. Wiener, "Breadth-first crawling yields high-quality pages," presented at the Proceedings of the 10th international conference on World Wide Web, Hong Kong, Hong Kong, 2001. [Online]. Available: https://doi.org/10.1145/371920.371965.
[26] D. Gupta and D. Singh, "User preference based page ranking algorithm," in 2016 International Conference on Computing, Communication and Automation (ICCCA), 29-30 April 2016 2016, pp. 166-171, doi: 10.1109/CCAA.2016.7813711.
[27] F. Alhaidari, S. Alwarthan, and A. Alamoudi, "User Preference Based Weighted Page Ranking Algorithm," in 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), 19-21 March 2020 2020, pp. 1-6, doi: 10.1109/ICCAIS48893.2020.9096823.
[28] M. Baker and M. Akcayol, "Priority Queue Based Estimation of Importance of Web Pages for Web Crawlers," International Journal of Computer Electrical Engineering, vol. 9, pp. 330-342, 07/27 2017, doi: 10.17706/ijcee.2017.9.1.330-342.
[29] Alexa. "The top 500 sites on the web." Amazın. https://www.alexa.com/topsites/countries (accessed 9:12:2021, 2021).