HDT Compression Mechanism for Efficiently Managing RDF datasets in Cloud environment

Abstract

By using cloud technology we possess various uses like high storage space, shared resources and  less management etc.  Data storage is a major issue when we receive large amount of data from variety of sources. Performing any task with huge amounts of RDF data in the cloud is a major task. Basically it seems to be a simple data model but it involves encoded  and complex graphs  which are mined at  both instance and schema-level data. Functioning or sharing this type of  data using classical techniques or dividing the graph using traditional min-cut algorithms leads to ineffective distributed operations. Diplo cloud technique is developed to provide better a solution for this problem. It uses a non-relational storage format and it semantically relates data patterns which are mined from both the instance level and schema level data to produce less inter-node operations. This is a effective mechanism to parse and index the data but it takes more bandwidth and storage space. To avoid this and to Provide a solution for this problem, we introduce HDT compression mechanism. HDT mechanism is a efficient technique for  querying and parsing. It can execute many queries per second  and reduces the duplicate data by that it reduces storage space and bandwidth.