Basic data analytics in IoT

Data analytics intends to find events, usually in a streaming series of data. There are multiple types of events and roles that a real-time streaming analysis machine must provide. The following is a superset of analytic functions based on the work of Srinath Perera and Sriskandarajah Suhothayan (Solution patterns for real-time streaming analytics. Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems (DEBS '15). ACM, New York, NY, USA, 247-255. The following is an enumerated listing of these analytic functions:

• Preprocessing: This includes filtering out events of little interest, denaturing, feature extraction, segmentation, transforming data to a more suitable form (although data lakes prefer no immediate transformation), and adding attributes to data such as a tag (data lakes do need tags).
• Alerting: Inspect data, and if it exceeds some boundary condition, then raise an alert. The simplest example...