An efficient unstructured big data analysis method for enhancing performance using machine learning algorithm

Abstract

In this modern world, data mining technology holds an essential position in all the major Engineering fields. Handling of Unstructured Big Data is an essential task of this era. At present, making the maximum advantage of parallel processing know-hows and the task of rapid examination of huge data steadily and continuously transmitted or received from various sources is becoming popular or conventional. The big data analytics job is fragmented into smaller jobs and ran over tens, hundreds or thousands of product servers by the parallel processing architecture. This helps in maintaining the data center cost efficient and facilitates easy handling of the enormous work in an efficient way. In this paper, proposed solution takes online consumer purchase. The online system has unrivalled bank of data on online consumer purchasing behavior that can be mined from its 100 million customers accounts. They use customer click-stream data and historical purchase data of all those 100 million customers and each user is shown personalized results on customized web pages. For improving Big Data performance the Machine Learning Method i.e. K-Nearest Neighbour algorithm used to support to take good analysis. Hadoop simulator is used to solve this kind of task.

Research on Unstructured Text Data Mining and Fault Classification Based on RNN-LSTM with Malfunction Inspection Report

“…These algorithms have achieved good results in engineering practice. However, for more unstructured data found in practice [14][15][16][17] (i.e., the malfunction inspection report), traditional artificial means of massive original document annotation and classification are not only time-consuming, but are also unable to achieve the desired results. For this reason, it has not been possible to adapt development of network information needs to the grid.…”