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Table 4 Comparison between different knowledge extraction techniques. Stream processing applies different techniques to extract knowledge from arriving data. The techniques that best fit the envisioned mining scenario depends on the available data, on the training model, and on the expected goals of the processing

From: A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges

Technique Data collection Computing resource demand Concept rift Training Supervised or unsupervised Process Typical applications Related work
Mining Sampling and Summarizing Usually low No No Unsupervised Preprocessing Knowledge extraction from historical databases Gamaet al. [78], Gaber [81]
Online learning Feature extraction Medium Yes Yes Supervised and Unsupervised Classification, regression, or aggregation Knowledge extraction from online transactions Andreoni Lopez et al. [10], Lobato et al. [79], Hultenn et al. [83], Lu et al. [85], Masuyama et al. [86], Polikar [87], Polikar et al. [88], Aaron et al. [89]
Reinforcement learning Model Free Medium Yes Yes Unsupervised Reinforcement Performance of controlling actions Li et al. [90], Liu and Yoo [91], Tabrizi et al. [92], Chen and Qiu [93], Santos Filho et al. [94]
Deep Learning Implicit feature extraction High Yes Yes Supervised and Unsupervised Classification, regression, or aggregation Image processing Deng and Yu [95], Kwon et al. [96], Deng [97], Kim et al. [98], Sze et al. [99], Wang et al. [101], Wang et al. [102], Turgut et al. [103], Wang et al. [104]