Following are the highlights.
- How fast we need results? Real-time vs. batch or a combination.
- How much data to keep? based on use cases and incoming data rate, we might choose between keeping none, summary, or everything. Edge analytics is also a related aspect of the same problem.
- From analytics, do we want hindsight, insight or foresight? decide between aggregation and Machine learning methods. Also, techniques such as time series and spatiotemporal algorithms will play a key role with IoT use cases.
- What is our Response from the system when we have an actionable insight? show a visualization, send alerts, or to do automatic control.
- Finally, we discussed the shape of IoT data and few reusable scenarios and the potential of building middleware solutions for those scenarios.
Please find the full post from https://iwringer.wordpress.com/2015/10/15/thinking-deeply-about-iot-analytics/.