A Survey on Big RDF Graph by using Indexing Approaches in Big Data

Many public knowledge bases are represented and stored into RDF graphs, where users can issue structured queries on such graphs using SPARQL. With massive queries over large and constantly growing RDF data, it is imperative that an RDF graph store should provide low latency and high throughput for concurrent query processing. We present, a distributed graph-based RDF store that leverages RDMA-based graph exploration to provide highly concurrent and low-latency queries over large data sets. This is follows in three ways. First, provides an RDMA-friendly distributed key/-value store that provides differentiated encoding and fine-grained partitioning of graph data to reduce RDMA transfers. Second, the cost of one-sided RDMA operations is largely oblivious to the payload size to a certain extent so avoid the cost of expensive final join operations. Third, countering conventional wisdom of preferring migration of execution over data, it is seamlessly combines data migration for low latency and execution distribution for high throughput by leveraging the low latency and high throughput of one-sided RDMA operations. An RDF stream is a sequence of RDF graphs, including associated metadata, as a flexible mechanism to add time-related metadata. RSP is to define a common model for producing, transmitting and continuously querying RDF Streams. It is continuous queries over streams of RDF data to improve the low latency and high throughput, it is better than prior system.