Accelerating Performance with the Artificial Intelligence of Things
Figure 5: Event Stream Processing Analyzes Data in Motion
transport or store it – a must for
many uses in the sensor driven world
of IoT devices and services.
Deploy Intelligence Where the Application
Needs It
The use cases described earlier entail data
that is constantly changing and in motion
(such as a driver’s geolocation or
temperature inside a data center) as well as
other discrete data (such as customer
profiles and historical purchase data). This
reality calls for analytics to be applied in very
different ways for different purposes. For
example:
It’s a multi-phase analytical approach. The
key principle to remember is not all data
points are relevant and not all need to be
sent to permanent storage. Sometimes the
question calls for complex analytics, and
sometimes speed is more important.
Sometimes the data must be analyzed at the
edge, and sometimes it needs to go back to
a data center. The analytics infrastructure
must be flexible and scalable to support all
those needs today and into the future.
High-performance analytics does the
heavy lifting on data at rest, in the
cloud or otherwise in storage.
Streaming analytics analyzes large
amounts of diverse data in motion,
where only a few items are likely to
be of interest, the data has only
fleeting value, or when speed is
critical, such as sending alerts about
an impending collision or component
failure.
Edge computing enables a system to
act on the data immediately, at the
source, without pausing to ingest,
IIC Journal of Innovation
Combine AI Technologies
To realize the highest returns with AIoT, look
beyond deploying a single AI technology.
Take a platform approach where multiple AI
capabilities work together, such as machine
learning and deep learning, for natural
language processing and computer vision.
For example, a research clinic of a large
hospital combines several forms of AI to
provide diagnostic guidance to its
physicians. The clinic uses deep learning and
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