Boris IoT slides
0 likes172 views
The document discusses lessons the Internet of Things (IoT) can learn from the life sciences. It notes that the life sciences have common data formats, ontologies to organize concepts, and open data repositories, unlike the fragmented IoT. The life sciences invested heavily in developing formal knowledge representations like the Gene Ontology to organize biological knowledge. Similarly, the IoT needs open standards for representing device capabilities and data to realize its potential of connecting everything.
Boris IoT slides
- 1. WHAT THE IOT SHOULD LEARN FROM THE LIFE SCIENCES BE OPEN OR MISS OUT! or
- 3. modified, image from http://www.householdappliancesworld.com health management air conditioning smart heating communications security entertainment lighting controlweather monitoring room occupancy
- 5. ‣ Everything is connected ‣ Big, noisy, often unstructured data www.thingslearn.com Analytics, context integration, machine learning and predictive modelling for the IoT.
- 6. 0 clean shirt left + washing machine estimates 97% of your last pack of powder used + it’s Wednesday, 23:55 + the last four Thursdays had a morning business meeting + the car is parked 20 m from a shop + last retail activity: 8 sec ago Send immediate text reminder to pick up washing powder + send tweet from @BorisHouse “need identified” AND “notification appropriate”Actionable insight. From everything.
- 7. health management air conditioning smart heating communications security entertainment lighting controlweather monitoring room occupancy everything is an app! no interfaces!
- 9. THE CONSUMER IOT FAILS FOR ITS LACK OF CONNECTEDNESS Matt Hatton, Machina Research The BLN IoT ‘14 Internet replaces wire It’s all about the connectedness M2M consumer IoT
- 10. • Computational biologist • Research group leader • Lecturer in genome biology • Advisor at Who is @BorisAdryan was EXPECTATION MANAGEMENT
- 11. DNA = storage of a blueprint RNA = ‘active copy’ of DNA protein = the building blocks of cells and tissues LIFE AS WE KNOW IT transcription translation Gregor Johann Mendel, exhibited in the Library at the NIMR
- 12. • Reading DNA information • Determining “the sequence of a gene” was a PhD in the early 1980s • Data processing was mainly transcribing the observation into a research paper BIOLOGY THEN AND NOW SEQUENCE INFORMATION Sanger sequencing ca. 1980 http://www.eplantscience.com
- 13. BIOLOGY THEN AND NOW SEQUENCE INFORMATION
- 14. BIOLOGY THEN AND NOW GENE ACTIVITY INFORMATION
- 15. 2 6 ATP • Signal transduction and metabolic pathways • Characterisation of proteins and substrates that mediate chemical reactions • Nobel prize material BIOLOGY THEN AND NOW BIOCHEMISTRY
- 16. • We know about 250k metabolites • 100k protein structures • on the order of 10k different chemical reactions BIOLOGY THEN AND NOW BIOCHEMISTRY
- 17. ‣ Everything is connected ‣ Big, noisy, often unstructured data ‣ We had studied how biological entities depend on each other
- 18. LIFE SCIENCE STRATEGIES DON’T WORK IN THE IOT - There are no commonly accepted - ‘catalogue’ of things, - ‘ontology’ of things, - ‘data format’ of things, - ‘meta data’ for things. - Most businesses are driven by revenue, not long-term strategic vision - Service providers have no need to publish - Data can be highly personal (cheap excuse) unless they’re
- 19. WE HAVE A PROBLEM WITH KNOWLEDGE REPRESENTATION
- 21. FORMALISING KNOWLEDGE WITH GENE ONTOLOGY
- 22. CURRENT GOVERNMENT INVESTMENTS INTO GENE ONTOLOGY NIH alone spent $44,616,906 on the ontology structure since 2001 (no data for UK/EU spendings) ~100 full-time salaries for experts with domain-specific knowledge ~40,000 terms
- 23. Oct. 1995 TOWARDS MIAMI AND DATA REPOSITORIES cf. IoT Nov. 1993
- 24. META DATA, SHARING AND DATA REPOSITORIES founded in Nov. 1999 But this is a complex and ambitious project, and is one of the biggest challenges that bioinformatics has yet faced. Major difficulties stem from the detail required to describe the conditions of an experiment, and the relative and imprecise nature of measurements of expression levels.The potentially huge volume of data only adds to these difficulties. Nature Feb. 2000 “ “ Nov. 2000 Oct. 2002 Wide adoption as requirement for publication in scientific journals
- 25. META DATA, SHARING AND DATA REPOSITORIES cf. IoT 2015 since 2003 Semantic Sensor Network Ontologyhttp://en.wikipedia.org/wiki/Silo
- 27. story measurements + meta data open, public repositories human curators ontology terms community PUBLISH OR PERISH ok? journal informal exchange - no credit! funders assessment The majority of this infrastructure is paid for by governments and charities industry!
- 29. measurements + meta data storage & provenance human curators ontology terms user PUBLISH OR YOU’RE NOT DOING IOT ok? Maybe the majority of this infrastructure should be paid for by governments? company cloud device registration “ “ privileges dataadded value
- 30. 0 clean shirt left + washing machine estimates 97% of your last pack of powder used + it’s Wednesday, 23:55 + the last four Thursdays had a morning business meeting + the car is parked 20 m from a shop + last retail activity: 8 sec ago Send immediate text reminder to pick up washing powder + send tweet from @BorisHouse “need identified” AND “notification appropriate”Actionable insight. From everything. “indicator of esteem” 3% left and not pressed “not home” “buying” credit card: “highly personal device” ~ alive and awake
- 32. THE INTERNET OF SUB-NETS? https://www.smartthings.com https://flic.io/partners
- 33. Dr. Boris Adryan @BorisAdryan @thingslearn @SoftwareSaved Open software Open source Open data Fellow of the Check out my talk on ‘Ontologies in the IoT’ on SlideShare: http://www.slideshare.net/BorisAdryan