Data Science At Scale for IoT on the Pivotal Platform

1© Copyright 2013 Pivotal. All rights reserved. 1© Copyright 2013 Pivotal. All rights reserved.
Data Science at Scale for IoT on the
Pivotal Platform
Oct 29, 2015
POSH meetup
Gautam Muralidhar
Rashmi Raghu
Srivatsan Ramanujam
Pivotal Data Science Team
Joint work with Pivotal Data Science team

2© Copyright 2013 Pivotal. All rights reserved.
Who Are We

Outline
 IoT – Present and Future
 Sensor Data Processing on Pivotal Stack
▪ Smoothing
▪ State estimation
▪ Edge detection
 Use Case – Smart Meter Analytics
 Use Case – Predictive Maintenance for Drilling
 Deploying your IoT Apps on PCF

Internet of Things – The
Present and Future

Picture credit (from L to R):
http://www.techlicious.com/blog/ericsson-mobility-report-internet-connected-devices/
http://www.mdpi.com/1424-8220/14/10/19260/htm
http://www.thehindubusinessline.com/info-tech/other-gadgets/care-for-a-connected-car/article5777444.ece
Devices are Increasingly Connected

How can these connected devices
in our home be smart enough to
make daily life easier?

How can we know a tree has
fallen on a power line before
the residents complain?

How can we use data
to 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

How does this…
…become this?
By recognizing thisFrom Sensors To Intelligence

How does this…
…become this?
By recognizing this
And by processing this
Sensors + Other Unstructured Data

To realize this opportunity requires the right
tools and techniques
Problem
Formulation
Modeling
Step
Data Step
Application
Step
Data Science for
Building Models
Sensors
&
Actuators
Data Lake

Sensor Data Processing on the
Pivotal Stack

Signal Processing Essential for IoT
Smoothing
Derivative computation
State Estimation
Interpolation
Frequency Decomposition
Edge and Ridge Detection
….

Smoothing
 Sensor data is typically noisy
 Smoothing becomes a
necessary first step prior to
computing other features of
interest
 How can smoothing be
performed in GPDB/HAWQ?

SQL Window Functions
group key 1 value (1, 1)
... ...
group key 1 value (1, n1)
... ...
group key 2 value (2, n2)
...
Partition (by
group key)
current row
current frame
Order by
value current row

Window Functions
 Think of “Sliding Windows”
 Calculation across rows related to the current row
– Similar to aggregate functions
– No grouping
 All window functions have an OVER clause
– ‘window’ of data to which the function applies (PARTITION BY)
– Ordering within a window partition (ORDER BY)
– Framing within a window partition (ROWS/RANGE)

Window Functions: The Syntax
Version 1
SELECT window_function() OVER (over clause)
Version 2
SELECT
window_function() OVER (window_name)
FROM
tablename
WINDOW window_name AS (over clause)

Smoothing with Window Functions Lag & Lead
https://github.com/gautamsm/data-science-on-mpp

Convolution with Window Functions
 Signal smoothing using averaging is an example of
Convolution
 The filter ‘g’ has equal weights to realize the ‘Average’ Low
Pass Filter
 The weights of ‘g’ can be pre-computed from other design
choices (e.g., a Gaussian) to realize a generic low pass filter

Other Operations With Window Functions
 Aggregate statistics
– Mean
– Median
– Standard Deviation
– Higher order moments
 Order statistics filters
 Interpolation

State Estimation and Gap Filling
 Kalman Filter – a well known probabilistic (Bayesian) state
estimation technique
 Optimal estimator if the motion model is linear and
measurement and process noise are Gaussian
 Sadly, the real world is far from linear and Gaussian
 Yet, there are many instances (e.g., navigational systems,
object tracking, etc.) where the simple Kalman filter does
remarkably well and hence the popularity

An Example Linear Motion Model
 A constant velocity (zero acceleration) linear motion model
is defined by the following state transition equations:
– x(t) = x(t-1) + vx(t) Δt
– y(t) = y(t-1) + vy(t) Δt
– vx(t) = vx(t-1)
– vy(t) = vy(t-1)
 x and y are observed quantities: could be geo-locations,
pixels in a video frame, etc.
 vx and vy are the directional velocities that are unobserved

Kalman Filter Design
 X (4 x 1): state vector [x, y, vx, vy]T
 State transition matrix (4 x 4):
F = [[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]]
 Z (2 x 1) : measurement vector from a position sensor [xm,ym]T
 Residual vector (2x1):
Y = Z-HX, where H is the 2 x 4 observation matrix
H = [[1, 0, 0, 0], [0, 1, 0, 0]]

Realizing a Kalman Filter
 Retrospective or offline processing: post sensor data
ingestion but prior to further analysis for machine learning
 Real time processing: during sensor data ingestion
 Offline processing: run on data from multiple sensors (e.g.,
multiple cars, multiple oil rigs, etc.)
– Can leverage an MPP environment such as Pivotal HAWQ or
Pivotal Greenplum Database (GPDB)

Detour: Technology and Tools
More on this here: http://www.slideshare.net/SrivatsanRamanujam/all-thingspythonpivotal

Data Science Toolkit
KEY LANGUAGES
P L A T F O R M
KEY TOOLS
MLlib
PL/X
ModelingTools
VisualizationTools
Platform

• For embarrassingly parallel
tasks, we can use procedural
languages to easily
parallelize any stand-alone
library in Java, Python, R,
pgSQL 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 : X in Python, R, Java,
C/C++ and pgSQL
• plpython and python are loaded as dynamic
libraries on the master and segment nodes
(libpython.so and plpython.so are under
$GPHOME/ext/python)

MADlib : Scalable, in-database Machine Learning
http://vldb.org/pvldb/vol5/p1700_joehellerstein_vldb2012.pdf

Functions
Supervised Learning
Regression Models
• Cox Proportional Hazards Regression
• Elastic Net Regularization
• Generalized Linear Models
• Linear Regression
• Logistic Regression
• Marginal Effects
• Multinomial Regression
• Ordinal Regression
• Robust Variance, Clustered Variance
• Support Vector Machines
Tree Methods
• Decision Tree
• Random Forest
Other Methods
• Conditional Random Field
• Naïve Bayes
Unsupervised Learning
• Association Rules (Apriori)
• Clustering (K-means)
• Topic Modeling (LDA)
Statistics
Descriptive
• Cardinality Estimators
• Correlation
• Summary
Inferential
• Hypothesis Tests
Other Statistics
• Probability Functions
Other Modules
• Conjugate Gradient
• Linear Solvers
• PMML Export
• Random Sampling
• Term Frequency for Text
Time Series
• ARIMA
Aug 2015
Data Types and Transformations
• Array Operations
• Dimensionality Reduction (PCA)
• Encoding Categorical Variables
• Matrix Operations
• Matrix Factorization (SVD, Low Rank)
• Norms and Distance Functions
• Sparse Vectors
Model Evaluation
• Cross Validation
Predictive Analytics Library
@MADlib_analytic

Kalman Filters in HAWQ/GPDB

Kalman Filter in HAWQ/GPDB
 Leverage an existing python library pykalman within a
PL/Python User Defined Function (UDF)
 The library provides an EM algorithm for estimating some of
the nuisance parameters of the Kalman
 Distribute the input table by a measurement run number
after aggregating the time series into arrays

SQL Call for Kalman Filter in HAWQ/GPDB

User Defined Functions – FFT Example
 UDFs can invoke sophisticated algorithms that can be run in a data parallel manner
 This example shows the use of R’s FFT algorithm (via spec.pgram function) to transform
signals into the frequency domain
 Example application: Smart meter power usage data transformed for periodicity analysis
CREATE OR REPLACE FUNCTION pgram_fn(tsval double precision[],taperval double precision)
RETURNS double precision[] AS
$$
rpgram <- spec.pgram(tsval,fast=FALSE,plot=FALSE,taper=taperval,detrend=TRUE)
rpout <- rpgram$spec
return(rpout)
$$
LANGUAGE 'plr’;

User Defined Functions – FFT Example
 Invoking the PL/R function to take advantage of data parallelism
 Aggregate data for each device of interest into arrays and distribute the result across all
segments by device
 FFT will then be computed in-place on each segment without need for data movement
CREATE TABLE pgram_table as
SELECT device_id, pgram
FROM (
SELECT device_id,
pgram_fn(input_array,0.0) as pgram
FROM smart_meter_ts_agg
) t1
DISTRIBUTED BY (device_id);
CREATE TABLE smart_meter_ts_agg AS
SELECT
device_id,
array_agg(reading order by ts) as input_array
FROM smart_meter_time_series
GROUP BY device_id
DISTRIBUTED BY (device_id);

Image Processing in HAWQ/GPDB

Leveraging OpenCV in MPP
 Many existing libraries with a rich collection of functions for
image processing and computer vision
 is one such library
 Leveraging existing libraries to process many images in
parallel in an MPP environment => faster app development

Example OpenCV Application
 We will consider an existing OpenCV
based application for edge detection
using Canny’s algorithm
 Edge detection – a very fundamental
and primitive operation
 Canny’s algorithm – a seminal
contribution by John F. Canny in
1986

Canny’s Algorithm in HAWQ/GPDB
 Problem setup – process several images stored in
HAWQ/GPDB tables in parallel
 Images stored as bytes in a column
 OpenCV calls wrapped inside a PL/C function

SQL Call for Canny’s Algorithm

Blogs on Image Processing in HAWQ/GPDB
PL/Python PL/C

Smart Meter Analytics
@Rashmi Raghu, Woo Jung, Kaushik Das, Vivek Ramamurthy

Utility Establishes Analytics to Reduce Annual
Cost of $100 Million in Energy Theft
Challenge:
• $100M annual loss due to energy theft associated with
Marijuana growth and inability to identify who is stealing power
• Inefficiencies in capital-intensive power grid because of
inaccurate view of demand to theft
• Enable energy balancing to smooth out grid consumption and
generation
Solution:
• Improve their business with data generated by smart meters to
perform analytics and present results to their customers
• Create a theft detection solution to prevent loss in revenue
from power usage
• Data Lake to accommodate massive growth of data and
analytics on existing infrastructure
Pivotal Solution includes: Pivotal GPDB, Pivotal HD
and Pivotal HAWQ

Smart Grid - Motivation
How can we detect a fallen tree
on a power line before residents
complain?
How can we balance
power supply and demand
more effectively?
How can we detect errors
and inefficiencies in grid
operations?

Analytics Use Cases of Value
Load Profiling
Anomaly / Outlier Detection
Revenue Protection (Theft)
Distribution Center Placement
Vegetation Management
Power Factor
 Load Profiling
– Profiling of power usage patterns
to enable better understanding of
customers, more granular
forecasting and planning of
demand response events
 Anomaly Detection
– Identify power usage patterns
that do not conform to normal
behavior

Load Profiling
 Objective: Cluster similar power usage patterns to
understand different types of normal behavior
 Step towards detecting anomalies within clusters
 Approach:
– Use periodograms of load profiles as feature vector
– Apply K-Means clustering algorithm using MADlib to group time
series with similar periodic behavior

Fast Fourier Transform: Time Domain
Time-series plots of Smart Meter Power Usage Data
1 cycle/day
2 cycles/day

Periodogram of a signal at frequency is defined as
Transforming Data Domain
P( fk/N ) = X( fk/N )
2
, k =1... N
k
N
Source: http://analog-eetimes.com/
f =1/12 f =1/6 f =1/4 f =1/3 f =5/12 f =1/2

Fast Fourier Transform: Frequency Domain
Period gram plots of Smart Meter Power Usage Data
1 cycle/day
2 cycles/day

In-database analytics: Load profiling
Time Series Smart Meter Data
Compute periodogram in PL/R
Cluster using k-means algorithm in MADlib with
periodograms as feature vector
Do any
clusters have
> N data
points?
End
Yes
NoGreenplum

Anomaly Detection
 Objective: Identify power usage patterns that do not conform to the
normal or expected behavior
 Revenue protection (theft), fault detection in meters, fault detection in
distribution networks
 Approach:
– Assume that normal data points in each cluster will lie close the centroid,
while anomalies will lie furthest away from centroid
– For each cluster, based on the distribution of the distances between the
centroid and data points, identify the points above the 99th percentile

Anomaly Detection
Compute the distance, D, between
each data point and the closest
centroid
Compute the natural log transform
of the distances, Y=ln(D)
Compute mean and standard
deviation of Y within each cluster
For a given
data point
with log-
distance y, is
P(Y>y)<=p?
Data point is
not an outlier
Yes
No
Check that Y is normally
distributed
Data point is
an outlier

Key Takeaways
 Built the foundation of a data-driven framework for anomaly
detection to leverage in revenue protection or demand
planning initiatives
 High Performance
– Pivotal Big Data Suite including MADlib and PL/R
– ~5 s to compute FFT for 100K meters (~300M readings)

Predictive Maintenance for Drilling
@Rashmi Raghu, Kaushik Das, Niels Kasch

Drilling into the San
Andreas Fault at Parkfield
California.
Credit: Stephen H.
Hickman, USGSData: 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*
• 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

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

ROP
Time
Drill equipment changes
Operational changes
Substrate changes
Operator Data
• Failure details
• RPM
Integrating
& Cleansing

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
Integrating
& Cleansing

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

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

Data Science At Scale for IoT on the Pivotal Platform

Data Science At Scale for IoT on the Pivotal Platform

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