Thursday, October 15, 2015

Thinking Deeply about IoT Analytics

Big data has solved many IoT analytics challenges. Especially system challenges related to large-scale data management, learning, and data visualizations. However, significant thinking and work required to match the IoT use cases to analytics systems.

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.

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