Title: | Statistical Search engine optimization |
Authors: | Hassan, A. |
Keywords: | материалы конференций;optimization;Internet;documentary database;Genetic Algorithm |
Issue Date: | 2018 |
Publisher: | БГУИР |
Citation: | Hassan, A. Statistical Search engine optimization / A. Hassan // BIG DATA Advanced Analytics: collection of materials of the fourth international scientific and practical conference, Minsk, Belarus, May 3 – 4, 2018 / editorial board: М. Batura [etc.]. – Minsk, BSUIR, 2018. – Р. 25 – 30. |
Abstract: | This report provides a view point of the development of optimization methods for the Retrieval of relevant information from a documentary database. As Genetic Algorithms (GA) are robust and efficient search and optimization techniques, they can be used to search the huge document search space. In this search, a general frame work of
information retrieval system and a development of optimization methods are to be discussed. With the rapid growth of
the amount of data available in electronic libraries, through Internet and enterprise network mediums, advanced methods
of search and information retrieval are in demand. Information retrieval systems, designed for storing, maintaining and
searching large-scale sets of unstructured documents. One step in optimizing the information retrieval experience is the
deployment of Genetic Algorithms, a widely used subclass of Evolutionary Algorithms that have proved to be a successful
optimization tool in many areas. The Evolutionary computation, Evolutionary Search Process, Genetic Operators, Genetic Programming, Evolutionary Techniques and Fuzzy Logic Principles in IRS, Fuzzy principles in Information are all
discussed. Establishment of statistical regularities in the study of search information in documentary database and a great
focus on the Search engine optimization (SEO) is the core of this study , meanwhile the genetic indexed along with the
indexing methods creation in the process of information search on the base of data obtained as a result of statistical
analysis performed queries to documentary database will be fully illustrated. |
URI: | https://libeldoc.bsuir.by/handle/123456789/31356 |
Appears in Collections: | BIG DATA and Advanced Analytics. Использование BIG DATA для оптимизации бизнеса и информационных технологий (2018)
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