BRScS Approach for Resolving Heterogeneity of Data from Multiple Resources at Semantic Level

Abstract

Data have multiplied at an exponential rate in the age of the Internet. Large amounts of data can be combined at this science hotspot. Making sense of big data has become increasingly difficult due to its volume, velocity, precision, and variety (sometimes referred to as heterogeneity). Many data sources are employed to create data heterogeneity. Big data fusion has both advantages and disadvantages when it comes to integrating data from a variety of sources. The focus of this work is on large data fusion using deep learning approaches to combine datasets from a variety of different sources. It is also possible to combine data from many sources. People are increasingly turning to the Internet and web-based services to meet their daily demands. Storage media can hold data in a variety of formats. Managing the vast volume of data is quite tough for an organization (referred to as “big data”). These data are rationally combined and incorporated into the system. Data fusion will be the subject of this paper. The process of collecting data and making judgments based on that data has become much more challenging as a result of technological advancements. The heterogeneity of data is made possible by the great volume, precision, and, most critically, variety of big data. A wide range of data sources can both help and hinder big-data converging. This study was created to introduce several methods and techniques for semantically merging huge datasets.