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DiploCloud Efficient and Scalable Management of RDF Data in the Cloud

DiploCloud Efficient and Scalable Management of RDF Data in the Cloud is an IEEE projects, here Cloud Technologies giving to information on DiploCloud Efficient and Scalable Management of RDF Data in the Cloud

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DiploCloud Efficient and Scalable Management of RDF Data in the Cloud

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  1. DiploCloud: Efficient and Scalable Management of RDF Data in the Cloud ABSTRACT Despite recent advances in distributed RDF data management, processing large-amounts of RDF data in the cloud is still very challenging. In spite of its seemingly simple data model, 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. EXISTING SYSTEM The complexity of scaling out an application in the cloud (i.e., adding new computing nodes to accommodate the growth of some process) very much depends on the process to be scaled. Often, the task at hand can be easily split into a large series of subtasks to be run independently and concurrently. Such operations are commonly called embarrassingly parallel. Embarrassingly parallel problems can be relatively easily scaled out in the cloud by launching new processes on new commodity machines. There are however many processes that are much more difficult to parallelize, typically because they consist of sequential processes. Disadvantages of Existing System: 1.Difficult to parallelize in practice. 2.Heavy Computation Cost. PROPOSED SYSTEM

  2. We propose DiploCloud, an efficient, distributed and scalable RDF data processing system for distributed and cloud environments. Our storage system in DiploCloud can be seen as a hybrid structure extending several of the ideas from above. Our system is built on three main structures: RDF molecule clusters (which can be seen as hybrid structures borrowing both from property tables and RDF subgraphs), template lists (storing literals in compact lists as in a column- oriented database system) and an efficient key index indexing URIs and literals based on the clusters they belong to. Contrary to many distributed systems, DiploCloud uses a resolutely non-relational storage format. Advantages of Proposed System: 1.A new hybrid storage model that efficiently and effectively partitions an RDF graph 2.Low computation require for hashing, template list, key index. SYSTEM ARCHITECTURE Cloud Admin: Admin is a main user of our cloud application. Admin add data of a students like his/her name, email, contact, courses etc in RDF file, this will help to distribution of cloud data. So based on

  3. key attribute we convert it to hash id to form subject. Those subjects and templates will add to hash table(Key Index). Admin can view the rdf and cloud data. User: Here user may faculty or student. They can login to application using login credentials, and those will get by signup. Here user is end user of our application. According to their access privileges they access data, like student can see his own data, faculty can search and view total datasets of all students. Storage Model Our storage system in DiploCloud can be seen as a hybrid structure extending several of the ideas from above. Our system is built on three main structures: RDF molecule clusters (which can be seen as hybrid structures borrowing both from property tables and RDF subgraphs), template lists (storing literals in compact lists as in a column-oriented database system) and an efficient key index indexing URIs and literals based on the clusters they belong to. SYSTEM REQUIREMENTS HARDWARE REQUIREMENTS: Hardware : Pentium Speed : 1.1 GHz RAM : 1GB Hard Disk : 20 GB SOFTWARE REQUIREMENTS: Operating System : Windows Family Technology : Java and J2EE Web Technologies : Html, JavaScript, CSS Web Server : Tomcat Database : My SQL Java Version : JDK 1.7 or 1.8 Implemented by

  4. Development team : Cloud Technologies Website : http://www.cloudstechnologies.in/ Contact : 8121953811, 040-65511811 About Cloud Technologies: Cloud Technologies is one of the best software development company. Which is providing IEEE, real time projects and training for M-Tech, B-Tech, ME, BE, BCA, MCA students and freshers in Hyderabad Ameerpet. Our aim is to ensure you receive the best commanding on subject.

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