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Article summary:
1. The Internet of Things (IoT) is rapidly growing, with an estimated 25-50 billion devices being used by 2020.
2. Big Data generated by IoT is characterized by velocity, multiple modalities, and varying data quality.
3. Machine learning algorithms can be used to extract higher level information from IoT data, and a use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented as an example.
Article analysis:
The article provides a comprehensive overview of the potential of machine learning for Internet of Things (IoT) data analysis. It presents a taxonomy of machine learning algorithms and discusses the challenges in IoT data processing. The article also includes a use case example to illustrate how Support Vector Machines (SVM) can be applied to Aarhus Smart City traffic data.
The article appears to be reliable and trustworthy overall, as it provides evidence for its claims and does not appear to contain any promotional content or partiality. The authors have also noted potential risks associated with using machine learning for IoT data analysis, such as privacy concerns and security issues. However, there are some points that could have been explored further in the article, such as counterarguments or alternative approaches that could be taken when dealing with the challenges in IoT data processing. Additionally, while the use case example provided is useful for illustrating how SVM can be applied to Aarhus Smart City traffic data, it would have been beneficial if more examples had been included to demonstrate how other machine learning algorithms can be used for different types of IoT applications.