Data Science Perspective and DS demo

BUILT FOR THE SPEED OF BUSINESS

2© Copyright 2014 Pivotal. All rights reserved.
Our everyday devices are smart and talk to us

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 ?

Making sense of your “big data”
Ÿ  Large volumes of data may be difficult to understand
–  ~100 tables
–  Tens of thousands of columns

–  ~100 tables
Ÿ  How do you build models that use all the data? Score all the
data?

–  ~100 tables
Ÿ  How do you build models that use all the data? Score all the
data?
Ÿ  Where do you focus your effort?
–  Getting a rapid grasp of relevant fields is important
–  Scanning lots of data is slow, creating models with huge numbers of features is
possible, but generally better to understand your data
–  Columns with little or no variation or only null values

What Can “Small Data” Scientists Bring on Their
“Big Data” Journey?
http://factspy.net/the-difference-
between-geeks-vs-nerds/

What Can “Small Data” Scientists Bring on Their
“Big Data” Journey?
Flat files
Distributed computing
HDFS
In-memory modelbuilding
Cloud computing
MapReduce
Command-line
tools
Databases
Command-line tools
Small Data Big Data
Many tools and
approaches are
being adapted to big
data technologies

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
•  A failure occurred at
the end of this run
BitpositionRPM
ROPWOB

Integrating
& Cleansing
Feature
Building
Modeling
•  A failure occurred at
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

How can noisy data create meaningful models?WOB
Time
WOB
ROP

Time
•  Deriving signal noisy sensor data
requires data cleansing

Time
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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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How can noisy data create meaningful models?Temperature
Time
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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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00:00 10:00 20:00 30:00 40:00 50:00 00:00
101520
df$ts_utc
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A cleansing approach:
use average across a window
•  Window functions in SQL allow
us to perform smoothing
seamlessly, at-scale
WOB
Time

Time
WOB
Time

Time
•  Test many hypotheses in parallel
to examine if features have an
effect on potency
WOB
Time

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)

Calling MADlib Functions: Fast Training, Scoring
Ÿ  MADlib allows users to easily and
create models without moving data
out of the systems
–  Model generation
–  Model validation
–  Scoring (evaluation of) new data
Ÿ  All the data can be used in one
model
Ÿ  Built-in functionality to create of
multiple smaller models (e.g.
classification grouped by feature)
Ÿ  Open-source lets you tweak and
extend methods, or build your own
SELECT madlib.linregr_train( 'houses’,!
'houses_linregr’,!
'price’,!
'ARRAY[1, tax, bath, size]’);!
MADlib model function
Table containing
training data
Table in which to
save results
Column containing
dependent variableFeatures included in the
model

out of the systems
model
'price’,!
'ARRAY[1, tax, bath, size]’,!
‘bedroom’);!
MADlib model function
Table containing
training data
Table in which to
save results
Column containing
dependent variableFeatures included in the
model
Create multiple output models
(one for each value of bedroom)

out of the systems
model
'price’,!
'ARRAY[1, tax, bath, size]’);!
SELECT houses.*,
madlib.linregr_predict(ARRAY[1,tax,bath,size],
m.coef!
)as predict !
FROM houses, houses_linregr m;!
MADlib model scoring function
Table with data to be scored Table containing model

Ÿ  The interpreter/VM of the language ‘X’ is
installed on each node of the HAWQ
Cluster
•  Data Parallelism:
-  PL/X piggybacks on HAWQ’s
MPP architecture
•  Allows users to write HAWQ/PostgreSQL
functions in the R/Python/Java, Perl, pgsql
or C languages Standby
Master
…
Master
Host
SQL
Interconnect
Segment Host
Segment
Segment
Segment Host
Segment
Segment
Segment Host
Segment
Segment
Segment Host
Segment
Segment
PL/X : X in {pgsql, R, Python, Java, Perl, C etc.}

Parallelized R in Pivotal via PL/R:
An Example
SQL & R
Ÿ  Parsimonious – R piggy-backs on Pivotal’s parallel architecture
Ÿ  Minimize data movement
Ÿ  Build predictive model for each state in parallel
TN
Data
CA
Data
NY
Data
PA
Data
TX
Data
CT
Data
NJ
Data
IL
Data
MA
Data
WA
Data
TN
Model
CA
Model
NY
Model
PA
Model
TX
Model
CT
Model
NJ
Model
IL
Model
MA
Model
WA
Model

An Example
Ÿ  With placeholders in SQL, write functions in the native R language
Ÿ  Accessible, powerful modeling framework

An Example
Ÿ  Execute PL/R function
Ÿ  Plain and simple table is returned

Aggregate and obtain
final prediction
Each tree makes a
prediction
Parallel Bagged Decision Trees

Genome Wide Association Study

SNP1

SNP2

SNP3

In-database genome-wide association study
Network
Interconnect
Master
Severs
Segment
Severs
SQL & R
Indiv Covariates
1 2 10
1 F 23 18
2 M 39 41
3 M 50 23
N F 19 24
COVARIATES
SNP
1 2 M
AA CC TT
AT CG TT
AA GG TC
TT CG TC

Network
Interconnect
Master
Severs
Segment
Severs
SNP1 SNP2 SNPM
SQL & R
Indiv Covariates
1 2 10
1 F 23 18
2 M 39 41
3 M 50 23
N F 19 24
Indiv SNP Geno
1 1 AA
2 1 AT
3 1 AA
1 2 CC
2 2 CG
3 2 GG
N M TC
COVARIATES GENOTYPES

Network
Interconnect
Master
Severs
Segment
Severs
SNP1 SNP2 SNPM
Pval1 Pval2 PvalM
SQL & R
Indiv Covariates
1 2 10
1 F 23 18
2 M 39 41
3 M 50 23
N F 19 24
Indiv SNP Geno
1 1 AA
2 1 AT
3 1 AA
1 2 CC
2 2 CG
3 2 GG
N M TC
COVARIATES GENOTYPES

Network
Interconnect
Master
Severs
Segment
Severs
SNP1 SNP2 SNPM
Pval1 Pval2 PvalM
SQL & R
Indiv Covariates
1 2 10
1 F 23 18
2 M 39 41
3 M 50 23
N F 19 24
Indiv SNP Geno
1 1 AA
2 1 AT
3 1 AA
1 2 CC
2 2 CG
3 2 GG
N M TC
SNP P-value
1 2.34x10-21
2 0.395
3 7.15x10-17
M 0.000142
COVARIATES GENOTYPES RESULTS

Network
Interconnect
Master
Severs
Segment
Severs
SNP1 SNP2 SNPM
Pval1 Pval2 PvalM
SQL & R
Indiv Covariates
1 2 10
1 F 23 18
2 M 39 41
3 M 50 23
N F 19 24
Indiv SNP Geno
1 1 AA
2 1 AT
3 1 AA
1 2 CC
2 2 CG
3 2 GG
N M TC
SNP P-value
1 2.34x10-21
2 0.395
3 7.15x10-17
M 0.000142
COVARIATES GENOTYPES RESULTS
•  In-database computation of ~1 million mutations for
thousands of individuals occurs rapidly and in parallel
•  Results are easily manipulated and explored

Visualize and analyze genomics data without movement
Generate relevant plots using tools like Tableau
immediately after parallel statistical analysis in-database
on Pivotal technology

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

Data Science Perspective and DS demo

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