Analytics in the IoT Context
In any non-trivial IoT use case, a huge volume of data is generated at a high speed. This high-volume data needs to be analyzed at similar speeds so that meaningful insights can be deduced, and the required actions can be triggered quickly. Most of the advancements in (generic) analytics can be applied directly to IoT use cases, but two key characteristics of data ingestion (that is, high volume and high frequency) necessitate that some special considerations are taken while reusing generic learnings/algorithms in the context of IoT. For example, IoT visualizations (dashboards) need to be displayed at reasonable granularity while not missing out on crucial/anomalous data points.
In addition to data volume and data velocity, IoT data is different as it can be a combination of structured (sensed values in time series format, such as temperature values captured at intervals of 1 second, and inventory data), semi-structured (operator comments), and unstructured...
