Performance Evaluation of Proposed Deep Ensemble Method Algorithms in Distributed and Traditional Computing Environments for Structured Data Analysis
Keywords:
Distributed Computing, Bigdata, Map Reduce, Deep Ensemble algorithm, Traditional Computing.Abstract
Storing big data is a complex because of the large amount, many types, and high speed at which the data is generated. This data may be imbalanced distributed data because it contains structured and unstructured data. To classify these data, it requires multiple technologies and methodologies to ensure efficient processing and retrieval of the data like Map Reduce model which is introduced in big data analysis. MapReduce is a parallel processing technique used to process data in a distributed manner. Specifically, it simplifies concurrent processing by partitioning large amounts of data into smaller segments and executing them simultaneously on a big data platform. However, it lacks efficiency compared to other forms of parallel processing and may exhibit slowness when performing specific operations. Additionally, it is not optimised for real-time processing, also it is unsuitable for applications that demand minimal latency. In the present era, distributed computing has emerged as a viable and increasingly popular choice for numerous applications of complex data. This is mostly owing to the technological advancements in computers, networks, mobile devices, and wireless communication technologies, which are becoming widely utilised in day to day lives. In this paper, proposed a Deep Ensemble method such as Deep Learning without Tuning, Deep Ensemble with boosting and Performance Tuning are applied to classifies the structured data in both distributed computing and traditional computing environments.
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