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Geo-IoT World, 25/05/16

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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.

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Geo-IoT World, 25/05/16

  1. 1. Data Analytics for IoT Context is Everything @BorisAdryan
  2. 2. Most things that people call IoT is actually M2M historian + analytics
  3. 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. 4. http://knowledge.openboxsoftware.com/blog/the- evolution-of-business-intelligence excerpt from Fast-forward 15 years… …when IoT really means “interconnected”
  5. 5. modified, image from http://www.householdappliancesworld.com health management air conditioning smart heating communications security entertainment lighting controlweather monitoring room occupancy
  6. 6. health management air conditioning smart heating communications security entertainment lighting controlweather monitoring room occupancy individual apps will make us dumb
  7. 7. source lost, seen on Twitter we want intelligent things that talk
  8. 8. David Rose: enchanted objects as UX paradigm modified, image from http://enchantedobjects.com
  9. 9. a solid scientific foundation no magic involved enchantment has
  10. 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. 11. time sleep monitor schedule location awareness building control mobilitycapacity weather prioritising planning provisioning artificial intelligence
  12. 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. 13. acquiring knowledge == learning machine learning creative thinking != decision making with statistics + algorithms ==
  14. 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
  15. 15. the hypothesis itself is a mathematical model
  16. 16. 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
  17. 17. 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?
  18. 18. 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
  19. 19. ontological root terms: function, process, localisation, proxy http://iot.ghost.io/four-branches-for-an-iot-ontology/
  20. 20. localisation static mobile indoor outdoor domestic MY house first floor kitchen living room second floor bedroom bathroom […] […] […] […] […] discrete descriptions continuous world <====
  21. 21. point perimeter geofence granularity and point of reference annotation & precision
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. AI players are getting ready
  26. 26. 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

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