data science perspective and ds demo

BUILT FOR THE SPEED OF BUSINESS

Additional Line 18 Point Verdana

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” �  Large volumes of data may be difficult to understand

–  ~100 tables –  Tens of thousands of columns




�  How do you build models that use all the data? Score all the data?




�  How do you build models that use all the data? Score all the data?

�  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 model building

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

Bit

posi

tion

RPM

RO

P W

OB





•  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? W

OB

Time

WOB

RO

P



OB

Time

•  Deriving signal noisy sensor data requires data cleansing



OB

Time


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00:00 10:00 20:00 30:00 40:00 50:00 00:00

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A cleansing approach: use average across a window


•  Window functions in SQL allow us to perform smoothing seamlessly, at-scale W

OB

Time



mpe

ratu

re

Time


•  Window functions in SQL allow us to perform smoothing seamlessly, at-scale W

OB

Time



mpe

ratu

re

Time


•  Window functions in SQL allow us to perform smoothing seamlessly, at-scale

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

WO

B

Time





Predict occurrence of equipment failure in a chosen future time window

Predict remaining life of equipment

Predict Rate-of-Penetration






•  Logistic Regression •  Elastic Net Regularized Regression (Binomial) •  Support Vector Machines

Predict remaining life of equipment








Predict remaining life of equipment •  Cox Proportional Hazards Regression








Predict remaining life of equipment •  Cox Proportional Hazards Regression

Predict Rate-of-Penetration •  Linear Regression •  Elastic Net Regularized Regression (Gaussian) •  Support Vector Machines


Calling MADlib Functions: Fast Training, Scoring �  MADlib allows users to easily and

create models without moving data out of the systems

–  Model generation –  Model validation –  Scoring (evaluation of) new data

�  All the data can be used in one model

�  Built-in functionality to create of multiple smaller models (e.g. classification grouped by feature)

�  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 variable Features included in the

model









'price’,!'ARRAY[1, tax, bath, size]’,!

‘bedroom’);!

MADlib model function Table containing

training data

Table in which to save results

Column containing dependent variable Features included in the

model Create multiple output models (one for each value of bedroom)









'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


�  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




PL/X : X in {pgsql, R, Python, Java, Perl, C etc.}


Parallelized R in Pivotal via PL/R: An Example

SQL & R

�  Parsimonious – R piggy-backs on Pivotal’s parallel architecture �  Minimize data movement �  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


Parallelized R in Pivotal via PL/R: An Example � With placeholders in SQL, write functions in the native R language

�  Accessible, powerful modeling framework


Parallelized R in Pivotal via PL/R: An Example �  Execute PL/R function

�  Plain and simple table is returned


Aggregate and obtain final prediction

Each tree makes a prediction

Parallelized R in Pivotal via PL/R: Parallel Bagged Decision Trees


Genome Wide Association Study


Genome Wide Association Study SNP1


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

SNP1 2 MAA CC TTAT CG TTAA GG TC

TT CG TC




Master Severs

Segment Severs

SNP1 SNP2 SNPM


1 2 10 1 F 23 18 2 M 39 41 3 M 50 23

N F 19 24

Indiv SNP Geno1 1 AA2 1 AT3 1 AA1 2 CC2 2 CG3 2 GG

N M TC

COVARIATES GENOTYPES




Master Severs

Segment Severs

SNP1 SNP2 SNPM

Pval1 Pval2 PvalM


1 2 10 1 F 23 18 2 M 39 41 3 M 50 23

N F 19 24


N M TC

COVARIATES GENOTYPES




Master Severs

Segment Severs

SNP1 SNP2 SNPM

Pval1 Pval2 PvalM


1 2 10 1 F 23 18 2 M 39 41 3 M 50 23

N F 19 24


N M TC

SNP P-value1 2.34x10-212 0.3953 7.15x10-17

M 0.000142

COVARIATES GENOTYPES RESULTS




Master Severs

Segment Severs

SNP1 SNP2 SNPM

Pval1 Pval2 PvalM


1 2 10 1 F 23 18 2 M 39 41 3 M 50 23

N F 19 24


N M TC

SNP P-value1 2.34x10-212 0.3953 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

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