Abstract
Large data storage has to serve high volume transactions of data everyday when users request the data that can cause latency. Therefore, intelligent methods are required to solve the insufficient data storage experienced by some providers. Pre-fetching technique is one of the best techniques that enable assuming the data will be needed by the user in the near future. Consequently, users easily access their data at high speed to avoid latency. However, pre-fetch the wrong objects cause slow down the data management performance. In this context, this research proposes Machine Learning (ML) techniques to predicting the pre-fetched objects accurately. This paper also compares the Rough Decision Tree (RDT) with others ML techniques including J48 Decision Tree, Random Tree (RT), Naïve Bayes (NB), and Rough Set (RS). The experimental results reveal the propose RDT performs better compared with RS single-alone. However, J48 performs well in classifying the web objects for IrCache, UTM blog data, and Proxy Cloud Storage (CS) data sets. Hence, J48 was proposing to be implementing into the future work of mobile cloud storage services.
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Acknowledgements
This research is supported by Ministry of Higher Education Malaysia (MOHE), Ministry of Science, Technology and Innovation Malaysia (MOSTI) and Universiti Teknologi Malaysia (UTM). This paper is financially supported by E-Science Fund, R.J130000.7928.4S117, PRGS Grant, R.J130000.7828.4L680, GUP Tier 1 UTM, Q.J130000.2528.13H48, FRGS Grant, R.J130000.7828.4F634 and IDG Grant, R.J130000.7728.4J170. The authors would like to express their deepest gratitude to IrCache.net and CICT, UTM for their support in providing the datasets to ensure the success of this research, as well as Soft Computing Research Group (SCRG) for their continuous support and fondness in making this research possible.
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Hussien, N.S., Sulaiman, S., Shamsuddin, S.M. (2017). Machine Learning Techniques for Prediction of Pre-fetched Objects in Handling Big Data Storage. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_12
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