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ISSN 1004-9037
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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      05 July-September 2023, Volume 38 Issue 4
    Article

    MANIFOLD SECURITY USING DATA DE-DUPLICATION AND CATEGORIZATION IMPLEMENTED IN STREAM PROCESSING AND BATCH PROCESSING
    Mrs. E.Kanimozhi, Dr. T. Prabhu
    Journal of Data Acquisition and Processing, 2023, 38 (4): 1461-1475 . 

    Abstract

    Abstract: Stream processing is a pivotal concept in modern data processing that revolves around real-time data ingestion, analysis, and transformation. It differs from traditional batch processing in that it enables the continuous and immediate handling of data as it flows in, rather than waiting for data to accumulate over time. Stream processing systems are adept at managing high data arrival rates and provide invaluable capabilities for applications such as real-time analytics, fraud detection, substantial data security and monitoring. Batch processing is a data processing method designed to efficiently handle large volumes of accumulated data, which have been stored over a specific timeframe. The data is processed in a single, organized batch and subsequently transmitted to an analytics system. This approach necessitates the use of storage solutions, such as databases or file systems, to manage and process finite, albeit substantial, data quantities, such as big data sets. The proposed work includes data de-duplication method in stream processing itself so that the de-duplication comparison and security provided in entry level itself. The categorization will be done in de-duplication process and can better optimize their data processing workflows thus providing light weight security to sustain speed. When coming to categorization shuffling will be done inside categories and as next steps inter category shuffling also done to provide manifold security. These systems are engineered to process and respond to data in motion, offering businesses and organizations the ability to gain actionable insights and make timely decisions based on live data streams. This proposed Manifold Security Algorithm encapsulates the essence of stream processing, emphasizing its significance in a data-driven world, Categorization, Substantial data pre-processing, providing cryptography security to ensure data security.

    Keyword

    Stream Processing, Batch Processing, Data De-Duplication, Categorization, Manifold Security and Substantial Data Processing


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ISSN 1004-9037

         

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