Secured Distributed Rdf Data In Cloud Storage

Abstract

Cloud computing is the use of computing resources (hardware and software) that are delivered as a service over a network (typically the Internet). The cloud computing uses networks of large groups of servers typically running low-cost consumer PC technology with specialized connections to spread data-processing chores across them. Consumer-oriented applications such as financial portfolios to deliver personalized information, to provide data storage.Speculate that large scaledata analysis tasks, decision support systems, and application specific data marts are more likely to take advantage of cloud computing platforms than operation. A distributed system is a model in which components located on networked computers communicate and coordinate their actions by passing messages. Efficient RDF data management is one of the cornerstones in realizing the Semantic Web vision. In the past, different RDF storage advanced techniques like clustering or vertical partitioning on the predicates.RDF actually encodes rich and complex graphs mixing both instance and schema-level data. Sharding such data using classical techniques or partitioning the graph using traditional min-cut algorithms leads to very inefficient distributed operations and to a high number of joins. In this paper, we describe DiploCloud, an efficient and scalable distributed RDF data management system for the cloud. Contrary to previous approaches, DiploCloud runs a physiological analysis of both instance and schema information prior to partitioning the data. In this paper, we describe the architecture of DiploCloud, its main data structures, as well as the new algorithms we use to partition and distribute data. We also present an extensive evaluation of DiploCloud showing that our system is often two orders of magnitude faster than state-of-the-art systems on standard workloads.