My keynote from the Location Intelligence session at Geo-IoT World in Brussels in May 2016. How location is one of many important context variables in the interpretation of sensor data.

1. Data Analytics for IoT
Context is Everything
@BorisAdryan

2. Most things that people
call IoT is actually M2M
historian + analytics

3. For a programmer, that
means…
wheel
loader
transmission: measurement
transmission: command
rules engine,
predictive analytics,
etc.
if measurement > X,
switch off
else
remain switched on
“easy”“hard”
connection failures,
latency, etc.
representation of
business logic

4. http://knowledge.openboxsoftware.com/blog/the-
evolution-of-business-intelligence
excerpt from
Fast-forward
15 years…
…when IoT really
means
“interconnected”

5. modified, image from http://www.householdappliancesworld.com
health
management
air conditioning
smart heating
communications
security
entertainment
lighting controlweather
monitoring
room occupancy

6. health
management
air conditioning
smart heating
communications
security
entertainment
lighting controlweather
monitoring
room occupancy
individual apps will make us dumb

7. source lost, seen on Twitter
we want
intelligent
things
that talk

8. David Rose: enchanted objects
as UX paradigm
modified, image from http://enchantedobjects.com

9. a solid
scientific
foundation
no
magic
involved
enchantment
has

10. For a programmer, that
means…
window
blinds
transmission: measurement
transmission: command
prioritising
planning
provisioning
“very hard”
“hard”
connection failures,
latency, etc.
inference of
business logic
sleep
tracker
calendar

11. time
sleep
monitor
schedule
location
awareness
building
control
mobilitycapacity
weather
prioritising
planning
provisioning
artificial
intelligence

12. raw data
information
knowledge
actionable
insight
action
reaction
barometric pressure, temperature, coordinates, schedule, …
snow storm coming, airport hotel, need to travel
flying and snow don’t go together
rebook flight
“context”
structure
rules
“learned”
dynamic
acquired

13. acquiring
knowledge
== learning
machine
learning
creative
thinking
!=
decision making
with
statistics +
algorithms
==

14. there’s no absolute
truth out there…
data
✓ hard facts
✓ intuitive
probability
✓ likelihood of some hypothesis
being true given the data
…but the better the data,
the better your prediction

16. the hypothesis
itself is a
mathematical
model

17. raw data
information
knowledge
actionable
insight
action
reaction
barometric pressure, temperature, coordinates, schedule, …
snow storm coming, airport hotel, need to travel
flying and snow don’t go together
rebook flight
“context”
structure
rules
“learned”
dynamic
acquired

18. if all you have is a hammer,
every problem looks like a nail…
I want to predict if my flight is going to be cancelled. What’s the temperature?

19. ontologies to the rescue!
used to establish
conceptual connection
between entities
knowledge inference
finger
ontology structure
- body part
- limb
- arm
- hand
- thumb
- finger
ontology rules
‣ controlled vocabulary
‣ clearly defined relationships
is a
is a
connects to
part of

20. ontological root terms:
function, process, localisation, proxy
http://iot.ghost.io/four-branches-for-an-iot-ontology/

21. localisation
static mobile
indoor outdoor
domestic
MY house
first floor
kitchen
living room
second floor
bedroom
bathroom
[…]
[…] […]
[…] […]
discrete
descriptions
continuous
world
<====

22. point
perimeter
geofence
granularity and
point of reference
annotation &
precision

23. the people factor
“Do you want to share
your location?”
what you think is
going to happen
what your user thinks
is going to happen

24. control and options
no location data
real-time low-
resolution data
time-averaged
historical data
(perimeter)
• per application/device
• inform user of consequences
• take into account when doing analytics
precise, real-time

25. immediate challenges
• accuracy of the technology
• conceptual issues of locality
• privacy concerns of the user
(no or agglomerated data)
• interoperability of IoT devices
• IoT assistants are in their infancy

26. AI players are getting ready

27. location is an important context
variable for IoT data analytics
@BorisAdryan
unfortunately, interoperability and
standards are still the key obstacles
in the consumer IoT ecosystem
the next wave of Siris and Cortanas may not live
in your phone, and will require detailed location
info around your assets