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Revealing the novel precise subset identification and deduplication of audio substance over the shared public environment

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Abstract

Cloud computing has become an effective solution for various services on the internet. Additionally, a cloud environment acts as a default storage location for application users. Large cloud storage service providers receive terabytes of data per second with an enormous amount of duplicated content. The duplicate copies can be eliminated using the deduplication technique. The proposed research work detects redundant audio content of the existing files in a cloud environment. Additionally, this study investigates the cloud computing environment which consists of numerous audio files (waveform audio file format). The proposed work detects redundant content and identifies only a part of the existing audio file, which refines the duplicated content over the space. This can be accomplished using the refined super subset identification algorithm, which processes a waveform audio file format content as numerical data and efficiently detects the repeated contents in an elastic cloud computing environment. The results demonstrate the accuracy of detecting duplicated files present in various files. The visual representation of the results proves the accuracy and exhibits that the quality of the audio content was not compromised. Finally, the method is effectively validated in a real-time environment.

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Acknowledgements

The study was supported by FIST grant received from the Department of Science and Technology, Government of India (Reference No. SR/FST/MSI-107/2015(C)).

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Authors and Affiliations

  1. Department of Computer Science, Srinivasa Ramanujan Centre, SASTRA Deemed University, Tamil Nadu, India

    K. Venkatesh

  2. Department of Mathematics, Srinivasa Ramanujan Centre, SASTRA Deemed University, Tamil Nadu, India

    D. Narasimhan

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Venkatesh, K., Narasimhan, D. Revealing the novel precise subset identification and deduplication of audio substance over the shared public environment. J Supercomput 78, 11856–11872 (2022). https://doi.org/10.1007/s11227-022-04317-6

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