Open AccessA SURVEY ON DATA INTEGRATION IN DISTRIBUTED WEB INFORMATION SYSTEM USING MACHINE LEARNING TECHNIQUES
Author(s) -
S Jinduja.,
V. Narayani
Publication year - 2022
Publication title -
epra international journal of multidisciplinary research
Language(s) - English
Resource type - Journals
ISSN - 2455-3662
DOI - 10.36713/epra9739
Subject(s) - computer science , data web , web intelligence , data integration , web modeling , machine learning , process (computing) , web page , domain (mathematical analysis) , web application , data mining , artificial intelligence , world wide web , mathematical analysis , mathematics , operating system
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. In our current advanced digital era it is very tedious process to identify an efficient integration of distributed sources in web information with the supported methodology towards enhanced time and space complexities. The existing web data based models are not effective in terms of distributed information processing, lack of optimal techniques due to the complexity in handling different web data resources, checking the effective integration output is also not feasible in the existing web data handling system. This paper presents a survey on Data Integration in Distributed Web Information System using Machine Learning Techniques. KEYWORDS—Segmentation, Web domain, Client structure, Machine learning, Performance