The research introduces an enhanced data mining model tailored for high-density large data storage systems. This model incorporates several crucial features to optimize data management: Firstly, it emphasizes aggregation and indexing, allowing data from diverse sources to be consolidated and indexed for rapid retrieval. This streamlined approach facilitates the storage of vast data volumes in a unified file. Secondly, the model incorporates advanced data filtering techniques, systematically eliminating redundant and superfluous data. These sophisticated filtering algorithms ensure that only pertinent and vital information is retained, thereby enhancing overall system efficiency. Additionally, data compression algorithms are employed to reduce data payload sizes by eliminating redundant information and compacting datasets. This compression strategy not only conserves storage space but also accelerates data query processes during data mining. Furthermore, the system leverages distributed storage clusters to store extensive high-density big data from various origins. This distributed storage architecture enhances data security, availability, and scalability across multiple nodes, thereby optimizing the data mining workflow. Lastly, paramount importance is placed on data security, incorporating encryption, access control, and authentication mechanisms to safeguard sensitive data and restrict access to authorized personnel.
K.P. Arjun, N.M. Sreenarayanan
GITAM University, Bengaluru Campus, India
Compression, Protection, Shrinking, Redundant
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| Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 4 , Issue: 4 , Pages: 491 - 497 )
Date of Publication :
September 2023
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138
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