Design and implementation Framework of Energy Efficient and Scalable RDF data Query Processing to Multi Server Query Processor (MSQP) in ELearnAPP

E-learning is learning new things through the use of technologies. It is growing at a rapid pace. Today more organizations are taking up e-learning. While e-learning technology developed extensively since its origin, there are numerous issues that experts find when come to executing e-learning Planning. One of the fundamental issues is the complexity of integrating these systems with content and with different type of business systems. RDF is a data model for representing labeled directed graphs, and it is an important building block of semantic web. Due to its flexibility and relevance, RDF has been utilized as a piece of e-learning. In these applications, large-scale graph datasets are extremely normal. Notwithstanding, existing techniques are not effectively managing them. We introduce a query processing system using Parallel Web Server, it consists of two noteworthy modules (1) The Master node and (2) Worker Nodes. The Master node investigates and analyzes the RDF data and places parts of data over multiple servers. The Worker Nodes parses the user query and distributes sub queries to cluster nodes. Also, the results of sub queries from various servers are gathered (and re-evaluated if necessary) and delivered to the user. Parallel Web Server goes for process queries by their deadlines, and preferred advantage high-level scheduling data to reduce the CPU energy consumption of a query-processing node. MSQP construct its decision on query efficiency predictors, estimating the processing volume and preparing time of a query. In e-learning ecosystem can assist organizations to achieve the advantages of an integrated approach to develop e-learning systems. It can be utilized for building a virtual environment for both educating and learning.