We review the data stream processing tools and frameworks that can be used to process such data online or on-the-fly along with their pros and cons, and their integrability with de facto data processing frameworks. We also investigate the importance of traffic data analytics and highlight the advantages of online learning in this regard, as well as the challenges associated with OL-based network traffic stream analysis, e.g., concept drift and the imbalanced classes. In this paper, we investigate and compare the OL techniques that facilitate data stream analytics in the networking domain. Online machine learning (OL) techniques promise to support such type of data analytics.
There is an urgent need for data analytic methods that can perform network data processing in an online manner based on the arrival of new data. Analyzing this data is essential for various purposes, such as network resources management and cyber-security analysis. Modern networks generate a massive amount of traffic data streams.
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