A proposed multi criteria indexing and ranking model for documents and web pages on large scale data

Due to the expansion of data, search engines encounter different obstacles for retrieving better relevant content to user's search queries. Consequently, various retrieval and ranking algorithms have been applied to satisfy the result's relevancy according to user's needs. Unfortunately, indexing and ranking processes face several challenges to achieve highly accurate results, since most of the existing indexes and ranking algorithms crawl documents and web pages based on limited number of criteria that satisfy user needs. So, this research studies and observes how search engines work and which factors contribute to higher rankings results. The research also proposes a Multi Criteria Indexing and Ranking Model (MCIR) based on weighted documents and pages which depend on one or more ranking factors, aiming at building a model that achieves high performance, better relevant pages, and the ability to index and rank both online/offline pages and documents. The MCIR model was applied on three different experi-ments to compare documents and pages results in terms of ranking scores, based on one or more criteria of user's preferences. The results of applying MCIR model proved that final pages ranking results depend-ing on multi-criteria are better than using only one criterion, and some criteria have great effect on rank-ing results than other criteria. It was also observed that the MCIR model achieved high performance on indexing and ranking dataset up to 100 gigabytes.(c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).