12. What are the problems?
• Viewing past data has almost no value
• Most IoT startups ended up just collecting big data
• Very few killer applications which creates x5-x10 value
13. Hidden cost of IoT
Relational Data Time series 'Big' Data
(activity, time)(name, address)
14. Relational legacy
data
Big data (eg time-series data)
Database
eg. Postgres
Database
eg. Mongo,
Casandra
Application logic (to split and combine data into
relational / non-relational part)
User Query
SQL query Time series
data query
Database architecture of IoT
@copyright: Rudradeb Mitra
28. IoT Architecture with Machine Learning
Database
Gamification
Social Engagement
Add a switch to turn off/onView Trip
Predictive +
Analytics
Machine Learning
Algo
@copyright: Rudradeb Mitra
29. What are the values?
• Find risky drivers and adjust their premiums so that majoity pay less
• Predict short and long term future to make roads safer by changing
behaviors
32. Patterns of risky drivers - Clustering
Picture taken from: http://www.ai-junkie.com/ann/som/som1.html
Find patterns in data
33. Self Organizing Maps (Clustering)
0.1
0.3
- Euclidean Distance between
training vector and weights
- The best match node is selected
- Adjust weights of neighboring nodes
to match this weight
0.55
0.15
0.31
0.49
Training
Data
0.6
0.9
0.8
@copyright: Rudradeb Mitra
34. Picture taken from http://www.ai-junkie.com/ann/som/som1.html
Most risky drivers
Most safe drivers
For the driving example
35. And how is that helpful?
• Premiums can be adjusted according to driving behavior and thus
majority will end up paying less
36. 2. Can we go further and predict short term future?
37. word2vec- Predicting next word
word2vec
- Does not understand words or
grammar
How
Feeling
You
Are
Today
Input words
embeddings
Predicted next word
Hidden
Layer
Output word
embedding
39. Example
word xi
N= 2
0
0
0
1
0 0.3 0 0.7 0 ... 0
0.1
0.77
0.39
.
.
0 0.29 0 0.55 0 ...0.3
0.5
0
Wi =
Wo =
Word embedding for Xi = X. Wi . Wo
Word embedding Xi = [0.33 ... 0.64 ]
0
0
0
1
X =
40. word2vec - 2d space
- Output matrix to 2
dimension using Principal
component Analysis
41. Picture taken from https://www.lucypark.kr/courses/2015-ba/text-mining.html
Germany - France + Paris
= Berlin
42. Prediction for drivers
score acceleration on Friday
score braking on Friday
word2vec
- Using the semantic association
between orders
(Product ID)
(Predicted score on
Sunday)
score braking on Saturday
score speed on Saturday
43. And how is that helpful?
• Advance short term warnings making roads safer and saving lifes
54. What can we achieve?
• Machine Learning can predict your future consumption (short term,
long term).
• If I know how much I will consume, I can save money and save
electricity!