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- W3203597739 abstract "Abstract An object-based cloud storage system is a storage platform where big data is managed through the internet and data is considered as an object. A smart storage system should be able to handle the big data variety property by recommending the storage space for each data type automatically. Machine learning can help make a storage system automatic. This article proposes a classification engine framework for this purpose by utilizing a machine learning strategy. A feature selection approach wrapped with a classifier is proposed to automatically predict the proper storage space for the incoming big data. It helps build an automatic storage space recommendation system for an object-based cloud storage platform. To find out a suitable combination of feature selection algorithms and classifiers for the proposed classification engine, a comparative study of different supervised feature selection algorithms (i.e., Fisher score, F-score, Lll21) from three categories (similarity, statistical, sparse learning) associated with various classifiers (i.e., SVM, K -NN, Neural Network) is performed. We illustrate our study using RSoS system as it provides a cloud storage platform for the healthcare data as experimental big data by considering its variety property. The experiments confirm that Lll21 feature selection combined with K -NN classifier provides better performance than the others." @default.
- W3203597739 created "2021-10-11" @default.
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- W3203597739 date "2021-09-25" @default.
- W3203597739 modified "2023-09-26" @default.
- W3203597739 title "Machine learning-driven automatic storage space recommendation for object-based cloud storage system" @default.
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- W3203597739 doi "https://doi.org/10.1007/s40747-021-00517-4" @default.
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