The document discusses an architecture integrating big data and machine learning through the FIWARE platform, highlighting specific algorithms like decision trees and random forests. It details the use and interconnectivity of components such as Apache NiFi and the Cosmos Generic Enabler for efficient data persistence and processing. Additionally, a practical use case is presented, predicting supermarket purchases using a random forest regression model, supported by an architecture based on the Orion Context Broker.

1. Big Data and Machine Learning with
FIWARE: An Architecture
Joaquín Salvachúa (joaquin.salvachua@upm.es)
Andrés Muñoz (joseandres.munoz@upm.es)
Sonsoles López (sonsoles.lopez.pernas@upm.es)
Gabriel Huecas (gabriel.huecas@upm.es)
Universidad Politécnica de Madrid
@jsalvachua, @anmunozx, @sonsoleslp, @ghuecas, @FIWARE

2. Map what we may do here
https://twitter.com/Ronald_vanLoon/status/1171741337579937794

3. Machine Learning Algorithm
● Each application field may require a different
algorithm
● Some solutions
have high
algorithm
complexity
● Some can be
parallelized in a
cluster (FlinkML )
● Other can use
GPU ( Tensorflow
for example)
● Even each case
could be different
we try to set up
some generic life
cycle.
https://www.datasciencecentral.com/profiles/blogs/a-tour-of-machine-learning-algorithms-1

4. ML Standard Solution
● Each problem requires an analysis of which ML algorithm suits our
data (so there can never be a full standard solution but something
that covers most cases).
● Later, the training dataset needs to be set up (even self-learning is
suitable for some cases).
● Each problem may be slightly different (“same same but different”).
● We can provide some solutions for some cases and use a proper
dataset (some anonymized datasets available are not suitable for ML
algorithms ).
● The tool to use (Spark, Flink, Tensorflow) depends on the chosen ML
algorithm.
● We are considering Apache Beam (google) as a wrapper over all
these technologies to be added into the FIWARE Cosmos ecosystem.

5. Simple Smart solutions: Reference
Architecture
Draco
Kurento
Wirecloud
QuantumLeap
Knowage
Flink
CrateDB

6. Spark Components
apache.org

7. Spark Scheduler
● Dryad-like DAGs
● Pipelines functions
within a stage
● Cache-aware work
reuse & locality
● Partitioning-aware
to avoid shuffles
join
union
groupBy
map
Stage 3
Stage 1
Stage 2
A: B:
C: D:
E:
F:
G:
= cached data partition

8. Apache Flink

9. ML Lifecycle

10. Decision Trees
● A decision tree is just what it says…
● Tree that is used to make decisions
● Kind of like a flow chart
● Each node is a test condition
● Each branch is outcome of test represented by corresponding node
● Leaf nodes contain the final decision
● Simple, simple, simple, …

11. Random Forest
● Random forest (RF) is generalization of a decision tree
● Decision tree is really, really simple
● Very intuitive and can be highly useful
● So, why do we need to generalize?
● Decision trees tend to overfit data
● Random forest avoids this problem
● But lose some of the intuitive simplicity

12. Random Forest Regression
https://towardsdatascience.com/random-forest-and-its-implementation-71824ced454f

13. Architecture

14. Draco: Persisting Context Data to MongoDB
Andrés Muñoz (joseandres.munoz@upm.es)
Universidad Politécnica de Madrid
@jsalvachua, @anmunozx, @sonsoleslp, @ghuecas, @FIWARE

15. ● The Draco Generic Enabler takes care of the data ingestion and
persistence. Is a is an easy to use, powerful, and reliable system for
processing and distributing data. Internally, Draco is based on
Apache NiFi.
● NiFi is a dataflow system based on the concepts of flow-based
programming. It supports powerful and scalable directed graphs of
data routing, transformation, and system mediation logic. It was
built to automate the flow of data between systems.
The Draco GE

16. Apache Nifi Architecture
17

17. Draco integration in the FIWARE
ecosystem
MiniNiFI (low profile version)
18

18. Features
● Based on Apache NiFi.
● NGSI 2 Support both for ingestion and serialization to have full
integration with the Orion Context Broker.
● Several persistent backends :
● MySQL, the well-know relational database manager.
● MongoDB, the NoSQL document-oriented database.
● PostgreSQL, the well-know relational database manager.
● HDFS, Hadoop distributed file system.
● Cassandra, Distributed database.
● CartoDB, for geospatial Data
● Templates for some common scenarios
● Rest API
19

19. 20
Basic Example

20. Demo

21. Cosmos: Loading Streaming Data
using Flink and Spark
Sonsoles López (sonsoles.lopez.pernas@upm.es)
Universidad Politécnica de Madrid
@jsalvachua, @anmunozx, @sonsoleslp, @ghuecas, @FIWARE

22. The Cosmos GE
The Cosmos Generic Enabler enables
an easier BigData analysis over
context integrated with some of the
most popular BigData platforms.
Features
✔ Batch Processing
✔ Stream Processing (Real-time)
✔ Direct data ingestion
✔ Direct connection with Orion
✔ Multiple Sinks
Orion
Context
Broker
COSMOS
DB HDFS
Web
service
Interface with the Internet of Things
(IoT), Robots and third-party systems
https://github.com/ging/fiware-cosmos

23. fiware-cosmos-orion-flink-connector
● https://github.com/ging/fiware-cosmos-orion-flink-connector
● https://github.com/ging/fiware-cosmos-orion-flink-connector-examples
ORION
Context Broker
Flink Cluster
Flink Job (JAR)
orion-flink-connector
HTTP POST (Notification)
HTTP
POST/PUT/PATCH
OrionSource
OrionSink

24. fiware-cosmos-orion-spark-connector
● https://github.com/ging/fiware-cosmos-orion-spark-connector
● https://github.com/ging/fiware-cosmos-orion-spark-connector-examples
ORION
Context Broker
Spark Cluster
Spark Job (JAR)
orion-spark-connector
HTTP POST (Notification)
HTTP
POST/PUT/PATCH
OrionReceiver
OrionSink

25. def main(args: Array[String]): Unit = {
val env = StreamExecutionEnvironment.getExecutionEnvironment
// Create Orion Source. Receive notifications on port 9001
val eventStream = env.addSource(new OrionSource(9001))
// Process event stream
val processedDataStream = eventStream
.flatMap(event => event.entities)
.map(entity => {
val temp = entity.attrs("temperature").value.asInstanceOf[Number].floatValue()
(entity.id, temp)
})
.keyBy(0)
.timeWindow(Time.seconds(10))
.aggregate(new Average)
// print the results with a single thread, rather than in parallel
processedDataStream.print().setParallelism(1)
env.execute("Temperature avg example")
}
Demo: Average temperature for each entity

26. Current status
Orion Connector
Orion Source/Receiver + Orion Sink ✔ ✔
RTD Documentation ✔ ✔
Unit Tests ✔ ✔
Examples ✔ ✔
Step-by-step tutorial ✔
Support NGSI LD

27. Big Data and Machine Learning with
FIWARE: Hands-on
Joaquín Salvachúa (joaquin.salvachua@upm.es)
Andrés Muñoz (joseandres.munoz@upm.es)
Sonsoles López (sonsoles.lopez.pernas@upm.es)
Gabriel Huecas (gabriel.huecas@upm.es)
Universidad Politécnica de Madrid
@jsalvachua, @anmunozx, @sonsoleslp, @ghuecas, @FIWARE

28. Predicting supermarket purchases
An use case

29. Purchase data
How we model each purchase:
● date
● client_id
● supermarket_id
● product_list
○ description
○ n_items
○ price

30. Data aggregation (for each store)
time day month year weekDay purchases
0 14 1 2016 3 5
1 14 1 2016 3 3
2 14 1 2016 3 4
3 14 1 2016 3 3
4 14 1 2016 3 2
5 14 1 2016 3 8
6 14 1 2016 3 12
7 14 1 2016 3 12
8 14 1 2016 3 23
9 14 1 2016 3 45
10 14 1 2016 3 55
11 14 1 2016 3 37
12 14 1 2016 3 42
13 14 1 2016 3 41
14 14 1 2016 3 38
15 14 1 2016 3 29
16 14 1 2016 3 33

31. Training our model
Random Forest
Regression
Algorithm
time
day
month
year
weekDay
purchases
Trained
Predictive
Model

32. Using our model to predict purchases
time
day
month
year
weekDay
?
Trained
Predictive
Model
purchases
Predicted number of purchases
in the given date and time

33. Architecture

34. Orion entities and subscriptions
{
"id": "ReqTicketPrediction1",
"type": "ReqTicketPrediction",
"predictionId": {
"value": 0,
"type": "String"
},
"socketId": {
"value": 0,
"type": "String"
},
"year":{
"value": 0,
"type": "Integer"
},
"month":{
"value": 0,
"type": "Integer"
},
"day":{
"value": 0,
"type": "Integer"
},
"time": {
"value": 0,
"type": "Integer"
},
"weekDay": {
"value": 0,
"type": "Integer"
}
}
{
"id": "ResTicketPrediction1",
"type": "ResTicketPrediction",
"predictionId": {
"value": 0,
"type": "String"
},
"socketId": {
"value": 0,
"type": "String"
},
"predictionValue":{
"value": 0,
"type": "Float"
},
"year":{
"value": 0,
"type": "Integer"
},
"month":{
"value": 0,
"type": "Integer"
},
"day":{
"value": 0,
"type": "Integer"
},
"time": {
"value": 0,
"type": "Integer"
}
}
Orion
Context Broker
Dracowww
Spark
Master
9001
3000 5050
ResTicketPrediction1
ReqTicketPrediction1

35. Get the code!
https://github.com/ging/fiware-global-summit-berlin-2019-ml

36. Open your laptop
Open your browser: http://localhost:3000
IT WORKS !!!!
git clone https://github.com/ging/fiware-global-summit-berlin-2019-ml
docker-compose up

37. Demo

38. Thank you!
http://fiware.org
Follow @FIWARE on Twitter
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