@inproceedings{772792ceb1d9429e988351852a1172c7,
title = "Research on data cache optimization based on time series state prediction",
abstract = "Data cache can reduce network congestion in a certain extent, and it can also reduce server load and user{\textquoteright}s access delay. However, the data cache is just passable in the cache hit rate and byte hit rate. It cannot play very well to accelerate query tasks response effect. Combining time series prediction method, this paper tries to predict the state of data using Autoregressive Integrated Moving Average Model and proposes a new cache strategy with Naive Bayes Classifier. Experimental results show that the new cache strategy is superior to the ID3 decision tree and BP neural network classifier in the precision and recall index. And compared with LRU algorithm, optimized cache strategy cannot only improve the cache efficiency, but also effectively improve the request hit rate of data cache.",
keywords = "ARIMA, Cache replacement, Cache strategy, Data cache, Naive bayes classifiers",
author = "Xiangxi Meng and Haoming Guo and Jianghua Lv and Shilong Ma",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 4th IEEE International Conference on Computer and Communication Systems, ICCCS 2019 ; Conference date: 23-02-2019 Through 25-02-2019",
year = "2019",
month = feb,
doi = "10.1109/CCOMS.2019.8821753",
language = "英语",
series = "2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "154--158",
booktitle = "2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019",
address = "美国",
}