This document describes how to use the IBM Bluemix Predictive Analytics service with Node-RED to enable collaboration between data scientists and developers for Internet of Things applications. It provides a step-by-step example of building a predictive model using sensor data from a TI SensorTag to detect failures, deploying the model on the Predictive Analytics service, and calling it from a Node-RED application. This allows data scientists to build models and developers to easily integrate predictive capabilities into their IoT solutions.

1. Using Bluemix Predictive Analytics
Service with Node-RED
Enabling I/T and Analytics convergence to tackle the
Internet of Things opportunities with agility
A step by step example
Lionel Momméja
Executive Architect
mommeja@fr.ibm.com
Global Industry Solutions Center
Nice-Paris

2. 2
Internet of Things solutions require an agile
collaboration between Data Scientists and Developers
• The internet will connect 50 billions devices over the next five years. The increase
in connectivity and access to real-time information is creating new opportunities
such as improving Return on Asset or enabling new disruptive business models
• The internet of things is generating a huge amount of data that can be processed
in real-time (or near real-time) for a better reactivity to events
• This create two new challenges: ability to handle new types of data sets
generated by the “things” and ability to process analytics models in near real-time
upon events reception.
• These challenges require an agile cooperation between two categories of actors
in the I/T which are not really used to work together so far: the data scientists and
the developers

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Introducing IBM Bluemix Predictive Analytics Service
IBM Predictive Analytics is a full-service Bluemix offering that makes it easy for developers
and data scientists to work together to integrate predictive capabilities with their applications.
Built SPSS analytics platform, Predictive Analytics allows to develop applications that make
smarter decisions, solve tough problems, and improve user outcomes
DeveloperData Scientist CRISP-DM Bluemix Garage Method

4. 4
In this Use Case, Data is coming from an IoT scenario built
on Texas Instrument SimpleLink™ SensorTag
• The SimpleLink™ SensorTag allows
quick and easy prototyping of IoT
devices.
• SensorTag can easily be connected to
IBM IoT Platform
• It supports the following sensors:
TI SimpleLink™ SensorTag
• Humidity and
Temperature
• Ambient & IR
Temperatures
• Barometric Pressure
• 3 axis Accelerometer
• 3 axis Gyroscope
• 3 axis Magnetometer
• 2 push Buttons
• 1 Luxometer

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Data Scenario
• The scenario is built on fictitious alerts created upon the following
combination of conditions:
– object temperature > 25 (Warm tag in your hand)
– x-axis accelerometer > 0.3 (Shake the take)
– light < 5 (Hide the tag in your hands)
• But we are not supposed to know yet those conditions…
• The data scientist will create and train a model that will discover them using
machine learning
Alert

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Build-time: Simple SPSS Stream aimed at detecting failures
from sensor data based on historical records
Training input data set
based on historical
records and - fictitious -
failure observations
Scoring branch highlighted in blue. Bluemix
Predictive Analytics service will discover this
branch at load time and execute it when invoked
Scoring input is typically
real-time or near real-time
IoT data collected on
Watson IoT Platform.
Filter out unnecessary fields specify input and target fields. Target is the alert
Chosen machine
learning algorithm is a
decision tree using Chi-
squared Automatic
Interaction Detection
(CHAID)
Model created after
execution of the CHAID
algorithm (training
branch)
Scoring output stored in a
table after execution of the
scoring branch
Training branch. This branch is executed at
build-time in SPSS to create the model.
MQTT
Watson IoT PlatformTI SimpleLink™ SensorTag
Historical records with failure alerts

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Build-time: Reviewing the CHAID Decision Tree Model
created after execution of the training branch
• When the training branch is executed, a CHAID
Decision Tree Model is created based on
historical data made of sensor’s data and
observed failures
• The CHAID Decision Tree is a machine learning
algorithm that will attempt to correlate sensor
data and failures
• This decision tree was created with this training
data set
• The machine learning algorithm established that
when acc_x > 0.29 and light <= 2 and
object_temp > 25 then there is a failure for
sure.

8. 8
Build-time: Testing the model in SPSS Modeler
Scoring input Failure predicted
by the model
Confidence
Score
Scoring input Confidence
Score
No failure predicted
by the model
When executing the model,
as expected, when all the
following conditions are
met, the model scores a
failure:
• object temperature > 25
• x-axis accelerometer > 0.3
• light < 5

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Deploying the model in Bluemix Predictive Analytics
Service
Click here to
open the
dashboard

10. 10
Deploying the model in Bluemix Predictive Analytics
Service
The service will
search for a scoring
branch
Context Id is a label that will be
used to identify your model in the
API

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Binding the Predictive Analytics Service to a Node-RED
application and retrieving service URL and Access Key
Click here to
open the app
dashboard
Select your predictive
analytics service…
This is my Node-RED application
Access Key
Service instance URL

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Accessing Predictive Analytics API Documentation
The Predictive Analytics service is a set of REST APIs called from any programming
language. You can access it here: https://console.ng.bluemix.net/docs/services/PredictiveModeling/index-gentopic1.html#genTopProcId2
POST http://{service instance}/pm/v1/score/{contextId}?accesskey={access_key for this bound application}
{ "tablename":“Scoring Input",
"header":["object_temp", "acc_x", "light"],
"data":[[27, 0.33, 1]]}
Body:

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Run-time: Invoking the Predictive Analytics Service
from Node-RED
Invoking the service is as simple as this:
Function node
http request node

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Run-time: Invoking the Predictive Analytics Service
from Node-RED
Reviewing service invocation results
{
"topic": "",
"payload": "[{"header":["object_temp","acc_x","light","$R-Failure","$RC-Failure"],"data":[[27,0.33,1,1,0.9846153846153847]]}]",
"_msgid": "840e7dcc.7bf18",
"headers": {
"x-powered-by": "Servlet/3.0",
"content-type": "application/json;charset=UTF-8",
"content-length": "113",
"date": "Thu, 12 May 2016 14:52:16 GMT",
"set-cookie": ["PASESSIONID=ffffffff0602630a45525d5f4f58455e445a4a421548;path=/;domain=palblyp.pmservice.ibmcloud.com;httponly"]
},
"statusCode": 200
}
Score
result
Score input Score
confidence

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Run-time: Parsing Predictive Analytics Service Results
To make parsing easy, use
JSON Node to convert json
into a javascript object
Accessing score result
is now simple
Score result
{
"topic": "",
"payload": "[{"header":["object_temp","acc_x","light","$R-
Failure","$RC-Failure"],"data":[[27,0.33,1,1,0.9846153846153847]]}]", …

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With IBM Bluemix Predictive Analytics Service and Node-
RED, Developers and Data Scientists can develop new IoT
Solutions with Agility
Developer Data Scientist

17. 17
Further Readings
• IBM Predictive Analytics service on Bluemix
• Engage Machine Learning for detecting anomalous behaviors of things an
IBM developerWorks recipe featuring Bluemix Apache Spark service, a
solution that can help scale-up in term of performance
• IBM SPSS Software
• Le parti pris des choses, a collection of prose poems on things from Francis
Ponge