Data Driven Action : A Primer on Data Science

Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a
Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
SPRINGONE2GX
WASHINGTON, DC
Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attributio n-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
Data Driven Action: A Primer on
Data Science
Sarah Aerni (@iTweetSarah)
Srivatsan Ramanujam (@being_bayesian)
Jarrod Vawdrey (@jjvawdrey)

Agenda
• Approaches and Open Source Tools for Wrangling and Modeling Massive
Datasets
• Sarah Aerni
• Text Analytics at Scale on MPP
• Srivatsan Ramanujam
• A Scalable Framework For Real Time Monitoring & Prediction Of Sensor Data
• Jarrod Vawdrey
2

Our everyday devices are smart and talk to us

Connected devices take action
to make daily life easier.
But what else?

How can IoT help prevent
accidents like the Macondo
Disaster ?

Gene Sequencing
Smart Grids
COST TO SEQUENCE
ONE GENOME
HAS FALLEN FROM
$100M IN
2001
TO $10K IN 2011
TO $1K IN 2014
READING SMART METERS
EVERY 15 MINUTES IS
3000X MORE
DATA INTENSIVE
Stock Market
Social Media
FACEBOOK UPLOADS
250 MILLION
PHOTOS EACH DAY
In all industries billions of data points represent
opportunities for the Internet of Things
Oil Exploration
Video Surveillance
OIL RIGS GENERATE
25000
DATA POINTS
PER SECOND
Medical Imaging
Mobile Sensors

Smart Systems = Sensors + Digital Brain + Actuators
Problem
Formulation
Modeling
Step
Data Step
Application
Step
Data Science for
Building Models
Sensors &
Actuators
Data Lake

How can data drive true, automated action?
How does this…

…become this?
How does this…

• How is data collected?
• Where is it stored and processed?
• Is there real signal or just noise?
• How can we build a predictive model?
• When is the right time to take action?

Critical considerations for successful modeling
How to build a
predictive model
at scale
Data-driven paradigms, data cleansing and feature engineering

How to build a
predictive model
at scale
Tradeoffs
between model
accuracy and
timeliness

How to build a
predictive model
at scale
Derive insight
from models to
change
processes
Tradeoffs
between model
accuracy and
timeliness

How to build a
predictive model
at scale
Use Cases
Oil Drilling Vaccine Manufacturing
Derive insight
from models to
change
processes
Tradeoffs
between model
accuracy and
timeliness
Treating Patients

Drilling into the San
Andreas Fault at Parkfield
California.
Credit: Stephen H.
Hickman, USGS
Data: The New Oil
• Oil & gas generates large amounts of data from sensors enabling data-
driven approaches to improve operations
Predictive maintenance
• Motivation: Failure costs estimated at $150,000/incident (billions
annually)*
• Goals
– Early warning system
– Insights into prominent features impacting operation and failure
– Reduction of non-productive drill time
– Reduced incidents
*http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry

How are models built using sensor data?
Integrating
& Cleansing
Feature
Building
Modeling

Integrating
& Cleansing
Feature
Building
Modeling
Integrated Data
Operator Data
( thousands of records )
• Failure details
• Component details
• Drill Bit details
Drill Rig Sensor Data
( billions of records )
• Rate of Penetration (ROP)
• RPM
• Weight on Bit (WOB)
Primary data sources

Integrating
& Cleansing
Operator Data
• Failure details
• RPM
ROP
Time

Integrating
& Cleansing
Operator Data
• Failure details
• RPM
ROP
Time
Drill bit changes

Integrating
& Cleansing
Operator Data
• Failure details
• RPM
ROP
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
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Operator Data
• Failure details
• RPM
WOB
Time
Integrating
& Cleansing

Operator Data
• Failure details
• RPM
WOB
Time
Integrating
& Cleansing
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
101520 df$ts_utc
df$wob
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Operator Data
• Failure details
• RPM
WOB
Time
Integrating
& Cleansing
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
101520 df$ts_utc
df$wob
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Integrating
& Cleansing
Feature
Building
Modeling
• A failure occurred at
the end of this run
BitpositionRPM
ROPWOB

Integrating
& Cleansing
Feature
Building
Modeling
the end of this run
• Taking a window of
time prior to failure,
what features should
we extract (e.g.
variance of RPM,
max bit position
velocity)?
Bitposition

Integrating
& Cleansing
Feature
Building
Modeling
the end of this run
• Taking a window of
time prior to failure,
what features should
we extract (e.g.
variance of RPM,
max bit position
velocity)?
RPM

Integrating
& Cleansing
Feature
Building
Modeling
Predict occurrence of equipment failure
in a chosen future time window
Predict remaining life of equipment
Predict Rate-of-Penetration

Integrating
& Cleansing
Feature
Building
Modeling
• Logistic Regression
• Elastic Net Regularized Regression (Binomial)
• Support Vector Machines
Predict remaining life of equipment

Integrating
& Cleansing
Feature
Building
Modeling
Predict remaining life of equipment • Cox Proportional Hazards Regression

Integrating
& Cleansing
Feature
Building
Modeling
Predict remaining life of equipment • Cox Proportional Hazards Regression
• Linear Regression
• Elastic Net Regularized Regression (Gaussian)

 Finding linear
dependencies between
variables
ROP = c0+ WOB * cWOB
0
10
20
30
40
50
60
70
80
90
100
110
-10 15
Rateof
Penetration
(ROP)
Weight on
Bit (WOB)
Linear Regression: Streaming Algorithm

 Finding linear
variables
0
10
20
30
40
50
60
70
80
90
100
110
-10 15
Rateof
Penetration
(ROP)
Weight on
Bit (WOB)

 Finding linear
variables
 How to compute with a
single scan?
0
10
20
30
40
50
60
70
80
90
100
110
-10 15
Rateof
Penetration
(ROP)
Weight on
Bit (WOB)

Linear Regression: Parallel Computation

Linear Regression: Parallel Computation
Segment 1 Segment 2

Linear regression on 10 million rows in
seconds
0
50
100
150
200
0 50 100 150 200 250 300 350
6 Segments
12 Segments
18 Segments
24 Segments
Hellerstein, Joseph M., et al. "The MADlib analytics library: or MAD skills, the SQL." Proceedings of the VLDB Endowment 5.12 (2012): 1700-1711.
# independent variables
Executiontime(s)

Big Data Machine Learning in SQL
http://madlib.net/
Predictive Modeling Library
Linear Systems
• Sparse and Dense Solvers
Matrix Factorization
• Single Value Decomposition (SVD)
• Low-Rank
Generalized Linear Models
• Linear Regression
• Multinomial Logistic Regression
• Cox Proportional Hazards
• Regression
• Elastic Net Regularization
• Sandwich Estimators (Huber white,
clustered, marginal effects)
Machine Learning Algorithms
• Principal Component Analysis (PCA)
• Association Rules (Affinity Analysis, Market
Basket)
• Topic Modeling (Parallel LDA)
• Decision Trees
• Ensemble Learners (Random Forests)
• Conditional Random Field (CRF)
• Clustering (K-means)
• Cross Validation
Descriptive Statistics
Sketch-based Estimators
• CountMin (Cormode-
Muthukrishnan)
• FM (Flajolet-Martin)
• MFV (Most Frequent
Values)
Correlation
Summary
Support Modules
Array Operations
Sparse Vectors
Random Sampling
Probability Functions
PMML Export

P L A T F O R M
Data Science Toolkit
KEY TOOLS KEY LANGUAGES
SQL

Opportunities for
Data-Driven Decisions
in Pharma

A pipeline of sensors and opportunities for optimizing output
Internet of Things in Manufacturing
Input materials Mix Incubate Filter Centrifuge Final Product

Sensors
Temp
Time
Absorbance
Elution volume
Velocity
Time

Temp
Time
Absorbance
Elution volume
Velocity
Time
• What opportunities exist for intervention, correction?
• Which attributes should be used as features in a model?
• When is the appropriate time to take action?

Temp
Time
Absorbance
Elution volume
Velocity
Time
• What opportunities exist for intervention, correction?
• Which attributes should be used as features in a model?
• When is the appropriate time to take action?
>6 months

Predicting vaccine potency using manufacturing data
Model generation and evaluation
True Potency
PredictedPotency
>6 months

True Potency
PredictedPotency
Data
Integration
Feature
Building
Modeling
>6 months

• Tracing product through pipeline
• Integrating manual and
automated data collection
• Missing data and outliers
Data
Integration
Feature
Building
Modeling
True Potency
PredictedPotency
>6 months

• Extract multiple features from
particular steps (duration, mean,
median, etc.)
• Considerations
• Tunable vs. measures
• Step in pipeline
Data
Integration
Feature
Building
Modeling
True Potency
PredictedPotency
>6 months
Temp
Time
Absorbance
Elution volume
Velocity
Time

True Potency
PredictedPotency
Data
Integration
Feature
Building
Modeling
>6 months
• Partial least squares
• Random forest
• Regularized regression

Interpreting the utility of a measure obtained during manufacturing based on model outcomes
Building insights from models
 Some features may
reveal tunable
parameters to alter
potency, others may
simply be markers
 Opportunities to
provide real-time
feedback on data
entry errors and
predicted potency
outcomesAssayed value Duration of a step
Potency
Potency
Correlation=0.45 Correlation=0.38

55© Copyright 2013 Pivotal. All rights reserved.
Internet of Things
in Healthcare
Improving Patient Outcomes
and Increasing Efficiency

Beyond monitor alerts for crashing patients–Prediction means prevention
Powering the Connected Hospital

1 53 42 6 7
8 1210 119 13 14
15 1917 1816 20 21
22 2624 2523 27 28
29 30
SUNDAY THURSDAYTUESDAY WEDNESDAYMONDAY FRIDAY SATURDAY
SEPTEMBER 2013
CDC – 2011- Number of Health Care Visits Per Year - Age Adjusted
31 2 4 5
6 108 97 11 12
13 1715 1614 18 19
20 2422 2321 25 26
27 3129 3028
SUN THUTUE WEDMON FRI SAT
OCTOBER 2013
1 53 42 6 7
8 1210 119 13 14
15 1917 1816 20 21
22 2624 2523 27 28
29 30
SEPTEMBER 2013
A Snapshot
1 2
3 75 64 8 9
10 1412 1311 15 16
17 2119 2018 22 23
24 2826 2725 29 30
NOVEMBER 2013
1 53 42 6 7
8 1210 119 13 14
15 1917 1816 20 21
22 2624 2523 27 28
29 3130
DECEMBER 2013
Another
Snapshot

What happens between doctor visits?
Blood
Glucose
Target Zone
31 2 4 5
6 108 97 11 12
13 1715 1614 18 19
20 2422 2321 25 26
27 3129 3028
OCTOBER 2013
1 53 42 6 7
8 1210 119 13 14
15 1917 1816 20 21
22 2624 2523 27 28
29 30
SEPTEMBER 2013
A Snapshot
1 2
3 75 64 8 9
10 1412 1311 15 16
17 2119 2018 22 23
24 2826 2725 29 30
NOVEMBER 2013
1 53 42 6 7
8 1210 119 13 14
15 1917 1816 20 21
22 2624 2523 27 28
29 3130
DECEMBER 2013
Another
Snapshot

The Promise of Internet of Humans
 Smart contact lenses and sensors to identify
and alert patients before catastrophic events
(e.g. blood sugar drop for diabetics)
 Wearables to track patient disease progression
using objective measures
 Track patient adherence
 Detect disease outbreaks using sequencing in
sewer system samples
 ECG monitoring on mobile phones for early
alerting of stroke

SPRINGONE2GX
WASHINGTON, DC
Scaling Java Applications for
NLP on MPP through PL/Java
Srivatsan Ramanujam
@being_bayesian

Text Analysis at Scale: Business Use Cases
62

Sentiment Analysis for Churn Prediction
Customer
A major telecom company
Business Problem
Reducing churn through more accurate
models
Challenges
• Existing models only used structured
features
• Call center memos had poor structure and
had lots of typos
Solution
• Built sentiment analysis models to predict
churn and topic models to understand topics
of conversation in call center memos
• Achieved 16% improve in ROC curve for
Churn Prediction

Predicting Commodity Futures using Twitter
Customer
A major a agri-business cooperative
Business Problem
Predict price of commodity futures through
Twitter
Challenges
• Language on Twitter does not adhere to
rules of grammar and has poor structure
• No domain specific label corpus of tweet
sentiment – problem is semi-supervised
Solution
• Built Sentiment Analysis and Text
Regression algorithms to predict commodity
futures from Tweets
• Established the foundation for blending the
structured data (market fundamentals) with
unstructured data (tweets)
http://www.slideshare.net/SrivatsanRamanujam/sramanujam-taw-2014

Platform and Tools
65

Data Lake
Business Levers
Apps
Pipeline of a Data Science Driven App
MLlib
PL/X
Model Building
Model Tuning
Continuous Model
Improvement
Data Feeds
Ingest Filter Enrich Sink
SpringXD
Greenplum

Pivotal Greenplum MPP DB
Think of it as multiple
PostGreSQL servers
Segments/Workers
Master
Rows are distributed across
segments by a particular field (or
randomly)

• For embarrassingly parallel tasks, we can
use procedural languages to easily
parallelize any stand-alone library in Java,
Python, R or C/C++
• The interpreter/VM of the language ‘X’ is
installed on each node of the MPP
environment
Standby
Master
…
Master
Host
SQL
Interconnect
Segment Host
Segment
Segment
Segment Host
Segment
Segment
Segment Host
Segment
Segment
Segment Host
Segment
Segment
Data Parallelism through PL/X
CREATE FUNCTION pymax (
a integer,
b integer)
RETURNS integer
AS $$
if a > b:
return a
return b
$$ LANGUAGE plpythonu;
SQL wrapper
Source
language code
Source
language
declaration
User Defined Functions

PL/X : Libraries
 We are able to tap into the vast collection of libraries from the open source
ecosystem in languages like Python, R and Java and apply those for data
parallel problems
PL/X
CoreNLP
http://www.slideshare.net/SrivatsanRamanujam/pivotal-data-labs-technology-and-tools-in-our-data-scientists-arsenal

Model Parallelism
 Data Parallel computation via PL/X libraries only allow us to run ‘n’ models in
parallel.
 This works great when we are building one model for each value of the group
by column, but we need parallelized algorithms to be able to build a single
model on all the available data
 For this, we use MADlib – an open source library of parallel in-database
machine learning algorithms.

MADlib: Scalable, in-database ML

GPText
Standby
SegmentSegment Segment Segment
…
Master
SQL
GPText
Scalable - one text processor instance per segment: “MPP
Text” - can linearly scale
High Availability – Replicated
Database management features
• Backup/Restore
• Online Expansion
• Data Recovery
• Performance Monitoring
Full Text Search - flexible indexing and
• search (stemming, phonetic search,
• multi-lingual search, etc.)
Join structured & text in one query
Advanced Text Analytics Platform
• Can be run ad-hoc
• Supports multiple machine learning algorithms
• Extensible

Sentiment Analysis of Tweets
73

Sentiment Analysis – Challenges
 Language on Twitter doesn’t
adhere to rules of grammar,
syntax or spelling
 We don’t have labeled data
for our problem. The tweets
aren’t tagged with sentiment
 Semi-Supervised Sentiment
Prediction can be achieved
by dictionary look-ups of
tokens in a Tweet, but
without Context, Sentiment
Prediction is futile!
“Cool”

Extracting Context – Part-of-speech tagging
 Part-of-speech tagging (POS tagging) helps us extract contexts associated
with sentiment words (an adjectives dictionary you may have access to).
 This simple approach was first described in a classic paper by Peter D.
Turney in 2002 and it is as follows:
1. Apply POS-tagging on sentences to tag words and their part-of-speech.
2. Extract 2-token phrases that provide context (ex: ADJECTIVE followed by NOUN,
NOUN followed by ADJECTIVE , ADVERB followed by a VERB)
3. Use a reference corpus to identify count of co-occurrence of your extracted
phrases with a strongly positive word like “excellent” compared to a strongly
negative word like “poor” and use that to compute a “polarity score”.
4. Sentiment associated with your sentence can be the average polarity score of all
phrases in your sentence.

Sentiment Analysis – Approach
1: Parts-of-speech Tagger : Gp-Ark-Tweet-NLP (http://vatsan.github.io/gp-ark-tweet-nlp/)
Phrase Extraction
Semi-Supervised Sentiment Classification
Phrasal Polarity
Scoring
Sentiment Scored
Tweets
Use learned phrasal
polarities to score
sentiment of new tweets
Part-of-speech
tagger1
Break-up Tweets into
tokens and tag their
parts-of-speech
 Parallelized ArkTweetNLP to achieve fast parts-of-speech tagging on Tweets
 Custom algorithm to extract contextual cues & score sentiment of tweets

DS Pipeline: Topic and Sentiment Analysis of
Tweets
77

Topic and Sentiment Analysis of Tweets
Stored on
Data Lake
Tweet
Stream
(PXF/gpfdist)
Loaded as
external tables
Parallel Parsing of
JSON and extraction
of fields using PXF
Topic Analysis
through MADlib
pLDA
Sentiment Analysis
through custom
PL/Python functions
Pivotal
Cloud Foundry
55 million
tweets/day
http://www.slideshare.net/SrivatsanRamanujam/a-pipeline-for-distributed-topic-and-sentiment-analysis-of-tweets-on-pivotal-greenplum-database

Spring XD Components
[user@smdw ~]$ xd-singlenode --hadoopDistro phd20
[user@smdw ~]$ xd-shell --hadoopDistro phd20
Spring XD SNE
Spring XD Shell
xd:> stream create --name gnipdecahose --definition "http --port=9009 | hdfs --
directory=/user/decahose/ --partitionPath=dateFormat('yyyy/MM/dd')" --deploy
Create Stream: HTTP Source, HDFS Sink
xd:> stream destroy --name gnipdecahose
Destroy stream

Parallel Parsing of JSON using PXF
Raw JSON

Parallel Parsing of JSON using PXF
http://blog.pivotal.io/pivotal/products/analyzing-raw-twitter-data-using-hawq-and-pxf
Natively parse JSON from external table

PL/Java: In-database parallel POS-tagging
UDT and UDF Usage

PL/Java: In-database parallel POS-tagging

Demo:
Topic and Sentiment Analysis of Tweets
84

Topic and Sentiment Analysis Engine (Demo)
http://www.slideshare.net/SrivatsanRamanujam/python-powered-data-science-at-pivotal-pydata-2013

SPRINGONE2GX
WASHINGTON, DC
A Scalable Framework For Real
Time Monitoring & Prediction Of
Sensor Data
Jarrod Vawdrey
@jjvawdrey

Connected cars can
produce more than
25GB of data per hour
18x F1 cars (100+ sensors
each) generated 243
terabytes of data from
their vehicles at the 2014
U.S. GRAND PRIX
GE jet engine produces
~1 terabyte of data on
a single cross country
flight
The Explosion Of Sensor Data
Organizations have started to apply sensors to all kinds of operational
equipment in order to gain added visibility into their day to day activities
87
http://www.bloomberg.com/bw/articles/2012-12-06/ge-tries-to-make-its-machines-cool-and-connected
https://www.hds.com/assets/pdf/hitachi-point-of-view-internet-on-wheels-and-hitachi-ltd.pdf
http://www.forbes.com/sites/frankbi/2014/11/13/how-formula-one-teams-are-using-big-data-to-get-the-inside-edge/
Examples

The Value Of Collecting Sensor Data
88
When used effectively, the data streaming off of sensors can be a huge
source of value, providing insights that generate additional revenue and
reduce operating costs.
UPS
• Through the use of
telematics UPS has been
able to optimize delivery
schedules and reduce gas
usage by 25 gallons per
driver per year. In the US this
will reduce fuel
consumption by 1.4 million
gallons annually.
US Government
• Using data collected from
sensors across data centers,
the General Services
Administration was able to
reduce total data center
power usage by 17%
(~$30k) in it’s USDA facility.
Dundee Precious Metals
• Outfitting miners and
machinery with internet
enabled sensors helped
DPM lower production
costs from $60 a ton to
$40.
http://www.automotive-fleet.com/article/story/2010/07/green-fleet-telematics-sensor-equipped-trucks-help-ups-control-costs/page/2.aspx
http://energy.gov/eere/femp/wireless-sensor-networks-data-centers
http://www.wsj.com/articles/mining-sensor-data-to-run-a-better-gold-mine-1424226463
Examples

Server Room Test Use Case
Problem: A single Supermicro 1U
Server (2x Xeon 2.66ghz
Processors, 64gb RAM, 4x 2TB
HDD) heats up a 6ftx4ft server
room (closet) to above safe
operating temperature (95oF) in
under two hours … the Hadoop
cluster in the test server room
had 4 servers + 2 switches!
89
Test Hadoop cluster

Solution: Add an air conditioning unit to the
server room and keep servers within the
41oF to 95oF safe operating temperature.
New Problem: 12amp AC requires
separate power circuit and breaker. If AC
trips breaker no guarantee that servers will
also shut off.
Still concerned with overheating & now
concerned with power consumption!
90
Tripp Lite Directed AC

Solution: Add sensors to server
room in order to …
• Monitor temperature remotely
• Predict & alert potential A/C and
server failures which could
cause overheating
• Optimize AC temperature
setting
91
USB Temperature Sensor
http://www.amazon.com/gp/product/B002VA813U?psc=1&redirect
=true&ref_=oh_aui_detailpage_o04_s00
Arduino LM35 Temperature Sensor
http://www.lightinthebox.com/digital-temperature-sensor-module-
ds18b20-for-arduino-55-125_p903326.html

Challenges Found With Adding Sensors
92
Challenges identified in the server room test environment are
commonly found in business critical applications
Data
Collection
• Sensor failure
• Sensor becomes
detached from
collection system
• Sensors may not
be collocated
• Integrating external
sources
Data
Volumes
• Handling large data
volumes
• Trade off between
granularity of data
and volume
• Data storage which
allows rapid access
for analysis and
modeling
Measurement
• Handling missing
values
• Building aggregate
metrics after
system failure
Machine
Learning
• Feature
engineering for
real time scoring
• Model
performance
testing and
retraining
indicators

Planning For Real Time Prediction: Two
Primary Approaches
93
Batch Modeling: Model developed
on a full training dataset and
published to scoring mechanism
Real Time Scoring: Model applied
to new data as it becomes available
to generate prediction
Each data point in a sequence is
used to update the model as it
becomes available and produce a
prediction
Batch Modeling & Real time Scoring
(Offline Learning) Online Learning
Hybrid approaches also exist

Tools Used: Spring XD
94
“Spring XD is a unified, distributed, and extensible system for data ingestion,
real time analytics, batch processing, and data export.”
http://projects.spring.io/spring-xd/
Key Concepts
• Streams: Defines a data pipe from source to sink, that may pass through multiple
processors
• Source: The data provider (e.g. HTTP, JDBC, RabbitMQ)
• Processor: Processing tasks operate on data being passed through a stream
• Sink: Termination point for data in a stream (e.g. HDFS, JDBC, File)
• Jobs: Batch processors launched from Spring-XD
• Taps: ‘Listen’ to data being passed through a stream

Tools Used: Other
95
Python RabbitMQ HDFS HAWQ R MADlib Redis
Programming
language
Message
broker
Hadoop
distributed
file system
SQL query
engine for
Hadoop
Statistical
programming
language/ap
plication
Scalable
SQL machine
learning
library
In-memory
data store
Scripts to
interfaces
with sensors
and send
readings to
RabbitMQ
Real time
model
scoring
Queue sensor
readings
Short term
readings
cache - if
connection to
Spring-XD
drops
Store
sensor
readings,
other data
and models
Access data
stored in
Hadoop
using SQL
Provide
access to R
for modeling
(via pl/r)
Batch
modeling
Batch
modeling
Short term
application
data storage
(e.g.
counters,
aggregates)

HDFS/H
AWQ
(Sink)
Framework For Monitoring & Modeling (Offline)
96
Spring-XD
S1
RabbitMQ–MessageBroker
(Source)
Real-time Monitoring
(Tap)
S2
SN
…
Batch
Model
Training
(Job)
Real-time Model
Scoring
(Processor)
Sensors
Python
Listeners
PN
P2
P1

HDFS/H
AWQ
(Sink)
Framework For Monitoring & Modeling (Online)
97
Spring-XD
S1
RabbitMQ–MessageBroker
(Source)
Real-time
Monitoring
(Tap)
S2
SN
…
Sensors
Python
Listeners
PN
P2
P1
Online
Learning
(Tap)

Server Room Test Use Case – Model
Development
98
Server Data
• 4x Rack Sensors
(USB)
• 1x Server Room
Sensor (USB)
• 1x Outside Room
Sensor (Arduino)
• 1x Outdoor Sensor
(Arduino)
• A/C settings
temperature
• Ganglia RRD data
(Server logs)
External Data
• Weather Underground
10 Day Forecast
Data
Cleanup &
Integration
Feature
Generation
Time series models
(ARIMA, VARs)
Event prediction models
(Log Reg, SVM)

Server Room Test Use Case - Application
• Node.js application used to
serve results remotely
• D3.js visualization of
observed and predicted
readings
• Python package ‘smtplib’
used to send email alerts
when failure or out of range
temperature event
predicted
99

Key Takeaways
100
• Organization who have embraced data collection from sensors
and are using this data to generate real time actionable insights
are already generating huge amounts of value
• Many challenges exist when working with streaming data which
can be solved for using a framework built around Spring-XD
• Flexibility to plug and play the best tool for the job is crucial in
implementing an scalable real time systems

http://blog.pivotal.io/data-science-pivotal
Check out the Pivotal Data Science Blog!

FOR FURTHER INFO, CHECKOUT…
• Pivotal Data Product Info, Docs and Downloads @ http://pivotal.io/big-data
• Pivotal Blog @ http://blog.pivotal.io
• Pivotal Data Science Blog @ http://blog.pivotal.io/data-science-pivotal
• Pivotal Academy @ https://pivotal.biglms.com
• Or reach out to your local Pivotal Account Executive…

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The following is intended to outline the general direction of Pivotal's offerings. It is
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or other planned modifications is subject to ongoing evaluation by Pivotal and is
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functionality, and should not be relied upon in making purchasing decisions
regarding Pivotal's offerings. These purchasing decisions should only be based on
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information in this presentation.
103

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