Challenges of IoT Analytics. Novelti approach to online Machine Learning for IoT networks. Kappa Achitecture.

1. www.novelti.io
IoT Intelligence as a Service

2. Agenda
•Introducing Novelti
•IoT characterization
•Tackling the processing problem
•Our architecture
•Machine learning for IoT
•Smart analytics

3. Novelti turns IoT data streams into actionable
intelligence.
It delivers in real-time, using online machine learning
algorithms, and provides a collaboration environment for
operation teams.

4. Problem: What makes NEST different?

5. What
defines IoT?

6. Characterizing IoT
•Simple devices
•Limited computing power
•Limited connectivity
•May have power limitations
•Expect failures

7. IoT Challenges
QoS
Reliability
Synchronization
Schema evolution
Out-of-order delivery

8. Let’s put it into perspective
•Number of variables: 4
•Time between measures: 30 s
•Malformed messages
•PoC stage
•Device malfunction
datasets from

9. What we expect
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IoT
enablement
IoT
data hubs
IoT
Smart operation

10. Architectural
challenges

11. Time
system time · device time · global clock
· UTC · summer time · GPS time ·
different time zones · universal time
zone · time evolution · timestamp ·
source synchronization · GMT

12. Scale
quality of service · timeseries storage
· long time archiving · elasticity · level
of service · security · stateful · fault-
tolerance · network partitioning ·
single point of failure

13. Streaming
time windows · real time · sliding
windows · microservice
synchronization · stateful · latency ·
bottleneck · stateless · multi-layer

14. Until now
IoT platforms
or
Big Data lambda architectures

15. IoT Platforms

16. Is your problem Big Data?
f( ) requires
distributed processing?

17. Lambda architecture
Batch Layer
Streaming Layer
New
Data
Serving
Layer

18. New
Data
The future - Kappa architecture
“…streaming systems combined with robust frameworks for
unbounded data processing will, in time, allow the relegation of the
Lambda Architecture to the antiquity of big data history where it
belongs” – Tyler Akidau
Streaming Layer
Serving
Layer

19. Our approach

20. Our Approach

21. Characteristics
•Enforces streaming processing
•Scalability and reliability
•Ability to scale phases independently
•Adapt to different SLA
requirements
•Supports geodistributed deployments

22. Machine learning
backend

23. Normalizer
+∞
-∞
Vmax
Vmin

24. Summarizer
Summary

25. Behavior identification
Summaryi

26. Streaming IoT Analytics
• SaaS streaming analytics
• Multivariate analysis
• Monitoring
• Alerts

27. Novelti
• Behavior
identification
• Behavior forecasting
• Profiling

28. www.slideshare.net/Noveltii
Thank you
www.novelti.io @novelti_io
@alvaroagea @dhiguero
alvaro@novelti.io daniel@novelti.io