Optimizing Business Insights: An Enhanced Large-Scale RDF Query Processing And Loading Speed On Big Data

Abstract

The generic access pattern is the Resource Description Framework (RDF), while SPARQL is a widely adopted query processing framework for collecting information. Due to its information adaptability and data modeling, this subject is presently receiving greater attention. The basic RDF structure is frequently used to supply web data through a spectrum of uses, including social networks, organizations' search engines, and other databases. A key component of semantic web managing data is the effective deployment of large-scale RDF querying. With the quicker growth of RDF data, storing all RDF triples in a particular node in larger datasets is frequently impossible. Due to this, researchers are concentrating on SPARQL query processing in distributed systems, particularly using the Hadoop system. The effectiveness of SPARQL query processing is increased by the application of the Map Reduce methodology. In this paper, we developed an Improved Large Scale RDF (ILS-RDF) for information partitioning to improve query processing and loading in an effective way. With the creation of ILS-RDF concepts and representations, knowledge transfer transitioned from the ordinary web to the semantic web. RDF is a widely used format for representing and querying linked data, and optimizing its processing on big data platforms requires careful consideration of data storage, query optimization, and integration with business tools. The proposed method uses low-grade indexing and run-time indexing for the phrases in the search to speed up data fetching and cut down on nodal connection latency.