FVI-BD: Multiple File Extraction using Fusion Vector Investigation (FVI) in Big Data Hadoop Environment

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V. Vadivu
N. Kavitha


— The Information Extraction (IE) approach extracts useful data from unstructured and semi-structured data. Big Data, with its rising volume of multidimensional unstructured data, provides new tools for IE. Traditional Information Extraction (IE) systems are incapable of appropriately handling this massive flood of unstructured data. The processing capability of current IE systems must be enhanced because to the amount and variety of Big Data. Existing IE techniques for data preparation, extraction, and transformation, as well as representations of massive amounts of multidimensional, unstructured data, must be evaluated in terms of their capabilities and limits. The proposed FVI-BD Framework for IOT device Information Extraction in Big Data. The unstructured data has cleaned and integration using POS tagging and similarity finding using LTA method. The features are extracted using TF and IDF. The Information extracted using NLP with WordNet. The classification has done with FVI algorithm.  This research paper discovered that vast data analytics may be enhanced by extracting document feature terms with synonymous similarity and increasing IE accuracy.

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How to Cite
Vadivu, V. ., & Kavitha, N. . (2023). FVI-BD: Multiple File Extraction using Fusion Vector Investigation (FVI) in Big Data Hadoop Environment. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 529–540. https://doi.org/10.17762/ijritcc.v11i7s.7032


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