Structure Implementation of Online Streams

View Sample PDF

Author(s): Ambika N. (St. Francis College, India)
Copyright: 2023
Pages: 12
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch134

Keywords: Data Analysis and Statistics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

Purchase

View Structure Implementation of Online Streams on the publisher's website for pricing and purchasing information.

Abstract

The previous contribution is rank-based monitoring and sampling methodology. It is based on data growth. It instantly discovers the mean variations in a means when only an insufficient division of searches is obtainable online. The measurement sequence will automatically enlarge knowledge for unobservable variables based on the online remarks. It wisely earmarks the monitoring sources to the most questionable input streams. The architecture can precisely gather the variables based on several noticeable variables and completely assemble a global monitoring statistic with the proposed augmented vector, which leads to a quick apprehension of the out-of-control state even if limited changed variables in real-time. It quickens the disclosure of method transfers in the circumstances of unfinished measurements by growing the unobservable learning with the dimensions of the marked ones. The suggestion aims to construct a structure based on the fed data. The present suggestion conserves 10.77% more energy and availability by 27.58% compared previous contribution.

The IRMA Community

Research IRM

Structure Implementation of Online Streams

Purchase

Abstract

Related Content

IRMA Sponsors