DiploCloud: Efficient and Scalable Management of RDF Data in the Cloud

Abstract

This research presents DiploCloud, a novel system designed for efficient and scalable management of Resource Description Framework (RDF) data in cloud environments. The system addresses the limitations of existing approaches by employing a distributed architecture leveraging cloud resources for parallel processing and optimized data storage. DiploCloud demonstrates significant improvements in query performance and scalability compared to traditional RDF stores, offering a robust solution for large-scale RDF data management. The evaluation showcases enhanced query response times and the ability to handle datasets orders of magnitude larger than those manageable by existing systems.

Introduction

The proliferation of Linked Data and the Semantic Web has led to an exponential growth in RDF data. Managing and querying this data efficiently poses significant challenges, especially when dealing with large-scale datasets. Current RDF stores often struggle with scalability and performance, particularly under heavy query loads. Cloud computing offers a promising solution, providing the resources necessary for handling massive datasets. However, effectively leveraging cloud resources for RDF data management requires specialized architectures and optimized algorithms. This research aims to address this gap by developing DiploCloud, a system designed to efficiently and scalably manage RDF data in cloud environments.

Objectives

• Develop a distributed, scalable architecture for managing large RDF datasets in the cloud.
• Implement efficient parallel query processing algorithms optimized for cloud environments.
• Evaluate the performance and scalability of DiploCloud against existing RDF stores.