Incremental Query Processing by Relevance Feedback Using Big-Data Streams

Authors

  • D. Ravi  Department of Computer Science and Engineering, Kathir College of Engineering, Coimbatore, Tamil Nadu, India
  • V. Viknesh  Department of Computer Science and Engineering, Kathir College of Engineering, Coimbatore, Tamil Nadu, India
  • A. Lakshmakarthi  Department of Computer Science and Engineering, Kathir College of Engineering, Coimbatore, Tamil Nadu, India
  • S. Sugumaran  Department of Computer Science and Engineering, Kathir College of Engineering, Coimbatore, Tamil Nadu, India

Keywords:

Query Optimiztion using Feedback Processing, Backtracking Process.

Abstract

This paper presents about social network in large scale distributed server data storing and retrival process is more complex.There has been an explosive increase in media data,such as images,videos and social media in the internet,mobile devices, and desktops.Engineers and researches are dealing with data sets of petabyte scale in the cloud computing paradigm.Thus, the demand for building a service stack to distribute,manage and process massive data sets has risen drastically.Data collection has become easy due to the rapid development of both mobile devices and wireless networks.During the processing of image queries. Many factor are affecting quality of the retrievel system. Image searching and ranking , indexing are the insufficient factors to affect the quality of image search results. There are many factors which affect the quality of image search results.The learning of the model is from the image output extracts the designed with the evolutionary feedback system to perform the image retrievel by processing the image search query.

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Published

2017-04-30

Issue

Section

Research Articles

How to Cite

[1]
D. Ravi, V. Viknesh, A. Lakshmakarthi, S. Sugumaran, " Incremental Query Processing by Relevance Feedback Using Big-Data Streams , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 2, pp.666-670, March-April-2017.