The data sets with multiple autonomous resources are popular in now a day. The big data plays a vital role in all the science and engineering departments such as physical, biological, biomedical science. Our main goal of this project is to reduce the storage space of the cloud when storing big data into the cloud server. In our proposed concept we are going to use the Huffman coding is used to effectively store the big data in the cloud server. Star Students ProjectsThe Huffman encoding algorithm is an optimal compression algorithm. Huffman encoding is an algorithm for the lossless compression of files based on the frequency of occurrence of a symbol in the file that is being compressed.In our proposed system the data with larger amount is compressed and it becomes a big data which is stored in a cloud server which reduces the data storage. The data is compressed and stored into the big data into the server. Our proposed system improves the scalability at the end of result. That means a process to handle a growing amount of work in a capable manner.
Star Students Projects
In our existing system the general purpose parallel program method used a weighted linear regression. It proposed a HACF (Heterogeneous Autonomous Couple x Evolving relationship) theorem. The heterogeneous was used the different collector which uses different protocols to manage system for recording. Each data is able to generate and collect information without involving any centralized control in autonomous. The value of big data was increased in the complex and evolving relationship. The existing system found the best feature from the entire feature present in the data. In our existing system the characteristics made it an extreme challenge for discovering useful knowledge from the Big Data. The existing work considered each individual as an independent entity without considering their social connections. That was one of the most important drawbacks of our existing system. The correlations between individuals inherently complicate the whole data representation and any reasoning process on the data. The most fundamental challenge for Big Data applications were to explore the large volumes of data and it requires the larger amount of storage space to store these big data.
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