Final Year IEEE Big data Projects in Chennai

Abstract :

The general value is increasing in Healthcare by adopting the Big data methods to research and understand its data from various sources. This perspective of improving healthcare services with big data offers a comprehensive view of system security and aspects determining various security violations. In the health industry, technological advancements came to a need for finding solutions as the increasing number of new drug resilient diseases, and shortage of medical staff, patient monitoring, and lifestyle changes are needed.

Abstract :

Accessing computing resources from the remote cloud in big data processing naturally sustains high end-to-end (E2E) delay for cloudlets are installed at the edge of networks can potentially ease this problem. Although load offloading in cloudlet networks has been proposed, placing the cloudlets to minimize the deployment cost of cloudlet providers and E2E delay of user requests is not addressed even now. The places and number of cloudlets and their servers have a critical impact on both E2E delay and deployment cost of user requests. We propose the Cost Aware cloudlet Placement in mobile Edge computing strategy to optimize the tradeoff between the E2E delay and deployment cost.

Abstract :

In this era of big data, a traditional scanning search pattern is gradually becoming unfit for satisfying user demands due to its long computing process. We propose a method known as sampling-based approximate search framework called the Hermes, that can meet the query demand for both accurate and efficient results. These metrics (ε,δ) Approximation are presented to uniformly measure efficiency and accuracy for Big data search service, enabling Hermes to work out a doable searching job. We can use bootstrapping techniques to further improve the speed of the process. The Incremental sampling strategy is explored to process homogeneous queries, and reuse theory is studied for a scenario of appending data. Theoretical analyses and experiments on a real-world dataset demonstrate that Hermes can give approximate results meeting the preset query requirements with high accuracy and efficiency.

Abstract :

There are 2.5 quintillions bytes of information is being produced daily, the period of Big data residues undeniably upon us. Running research on widespread datasets is an experiment. The key proportion of the accumulated information is a confident value of numerical implication in various cases. Censoring delivers the expected choice for data decrease. The information carefully preferred through censoring is not consistent, which forces a computational amount of necessity. We suggested lively, queuing techniques to evenly available the data’s dealing and surrendering the convergence act of censoring. And we will be using the AES Algorithms to encrypt given Data from file systems, and it will be uploading all files to the data center using the queuing model.

Abstract :

Big sensor data is prevalent for industry and scientific research applications where data generated with high volume and velocity is difficult to process using database management tools and traditional data processing applications. Cloud computing provides a promising platform to support addressing this challenge. It provides a flexible stack of massive computing, storage, and software services in a scalable manner at a low cost. Some techniques are developed for processing sensor data on the cloud, such as sensor-cloud. However, it does not provide efficient support, fast detection, and locating of errors. For fast data error detection in big sensor data sets, in this paper, we develop a novel data error detection approach that manipulates the full analysis potential of cloud outlets and the network feature of WSN. A set of sensor data error types are classified and defined. Based on this, the network characteristic of a huddled WSN is used and analyzed to reinforce fast error detection and location.