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 Architectmommeja@fr.ibm.com

Global Industry Solutions Center

Nice-Paris

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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

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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.

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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

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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 applicationAccess 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-REDInvoking 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-REDReviewing 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

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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