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Data Science At Scale for IoT on the Pivotal Platform

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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

Published in: Data & Analytics
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Data Science At Scale for IoT on the Pivotal Platform

  1. 1. 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. 2. 2© Copyright 2013 Pivotal. All rights reserved. Who Are We
  3. 3. 3© Copyright 2013 Pivotal. All rights reserved. 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
  4. 4. 4© Copyright 2013 Pivotal. All rights reserved. Internet of Things – The Present and Future
  5. 5. 5© Copyright 2013 Pivotal. All rights reserved. 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
  6. 6. 6© Copyright 2013 Pivotal. All rights reserved. How can these connected devices in our home be smart enough to make daily life easier?
  7. 7. 7© Copyright 2013 Pivotal. All rights reserved. How can we know a tree has fallen on a power line before the residents complain?
  8. 8. 8© Copyright 2013 Pivotal. All rights reserved. How can we use data to help prevent accidents like the Macondo Disaster ?
  9. 9. 9© Copyright 2013 Pivotal. All rights reserved. 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
  10. 10. 10© Copyright 2013 Pivotal. All rights reserved. How does this… …become this? By recognizing thisFrom Sensors To Intelligence
  11. 11. 11© Copyright 2013 Pivotal. All rights reserved. How does this… …become this? By recognizing this And by processing this Sensors + Other Unstructured Data
  12. 12. 12© Copyright 2013 Pivotal. All rights reserved. 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
  13. 13. 13© Copyright 2013 Pivotal. All rights reserved. Sensor Data Processing on the Pivotal Stack
  14. 14. 14© Copyright 2013 Pivotal. All rights reserved. Signal Processing Essential for IoT Smoothing Derivative computation State Estimation Interpolation Frequency Decomposition Edge and Ridge Detection ….
  15. 15. 15© Copyright 2013 Pivotal. All rights reserved. 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?
  16. 16. 16© Copyright 2013 Pivotal. All rights reserved. SQL Window Functions group key 1 value (1, 1) ... ... group key 1 value (1, n1) group key 2 value (2, 1) ... ... group key 2 value (2, n2) group key 3 value (3, 1) ... Partition (by group key) current row current frame Order by value current row
  17. 17. 17© Copyright 2013 Pivotal. All rights reserved. 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)
  18. 18. 18© Copyright 2013 Pivotal. All rights reserved. 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)
  19. 19. 19© Copyright 2013 Pivotal. All rights reserved. Smoothing with Window Functions Lag & Lead https://github.com/gautamsm/data-science-on-mpp
  20. 20. 20© Copyright 2013 Pivotal. All rights reserved. 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
  21. 21. 21© Copyright 2013 Pivotal. All rights reserved. Other Operations With Window Functions  Aggregate statistics – Mean – Median – Standard Deviation – Higher order moments  Order statistics filters  Interpolation
  22. 22. 22© Copyright 2013 Pivotal. All rights reserved. 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
  23. 23. 23© Copyright 2013 Pivotal. All rights reserved. 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
  24. 24. 24© Copyright 2013 Pivotal. All rights reserved. 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]]
  25. 25. 25© Copyright 2013 Pivotal. All rights reserved. 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)
  26. 26. 26© Copyright 2013 Pivotal. All rights reserved. Detour: Technology and Tools More on this here: http://www.slideshare.net/SrivatsanRamanujam/all-thingspythonpivotal
  27. 27. 27© Copyright 2013 Pivotal. All rights reserved. Data Science Toolkit KEY LANGUAGES P L A T F O R M KEY TOOLS MLlib PL/X ModelingTools VisualizationTools Platform
  28. 28. 28© Copyright 2013 Pivotal. All rights reserved. • 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)
  29. 29. 29© Copyright 2013 Pivotal. All rights reserved. MADlib : Scalable, in-database Machine Learning http://vldb.org/pvldb/vol5/p1700_joehellerstein_vldb2012.pdf
  30. 30. 30© Copyright 2013 Pivotal. All rights reserved. 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
  31. 31. 31© Copyright 2013 Pivotal. All rights reserved. Kalman Filters in HAWQ/GPDB
  32. 32. 32© Copyright 2013 Pivotal. All rights reserved. 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
  33. 33. 33© Copyright 2013 Pivotal. All rights reserved. SQL Call for Kalman Filter in HAWQ/GPDB https://github.com/gautamsm/data-science-on-mpp
  34. 34. 34© Copyright 2013 Pivotal. All rights reserved. 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’;
  35. 35. 35© Copyright 2013 Pivotal. All rights reserved. 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);
  36. 36. 36© Copyright 2013 Pivotal. All rights reserved. Image Processing in HAWQ/GPDB
  37. 37. 37© Copyright 2013 Pivotal. All rights reserved. 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
  38. 38. 38© Copyright 2013 Pivotal. All rights reserved. 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
  39. 39. 39© Copyright 2013 Pivotal. All rights reserved. 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
  40. 40. 40© Copyright 2013 Pivotal. All rights reserved. SQL Call for Canny’s Algorithm https://github.com/gautamsm/data-science-on-mpp
  41. 41. 41© Copyright 2013 Pivotal. All rights reserved. Blogs on Image Processing in HAWQ/GPDB PL/Python PL/C
  42. 42. 42© Copyright 2013 Pivotal. All rights reserved. Smart Meter Analytics @Rashmi Raghu, Woo Jung, Kaushik Das, Vivek Ramamurthy
  43. 43. 43© Copyright 2013 Pivotal. All rights reserved. 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
  44. 44. 44© Copyright 2013 Pivotal. All rights reserved. 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?
  45. 45. 45© Copyright 2013 Pivotal. All rights reserved. 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
  46. 46. 46© Copyright 2013 Pivotal. All rights reserved. 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
  47. 47. 47© Copyright 2013 Pivotal. All rights reserved. Fast Fourier Transform: Time Domain Time-series plots of Smart Meter Power Usage Data 1 cycle/day 2 cycles/day
  48. 48. 48© Copyright 2013 Pivotal. All rights reserved. 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
  49. 49. 49© Copyright 2013 Pivotal. All rights reserved. Fast Fourier Transform: Frequency Domain Period gram plots of Smart Meter Power Usage Data 1 cycle/day 2 cycles/day
  50. 50. 50© Copyright 2013 Pivotal. All rights reserved. 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
  51. 51. 51© Copyright 2013 Pivotal. All rights reserved. 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
  52. 52. 52© Copyright 2013 Pivotal. All rights reserved. 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
  53. 53. 53© Copyright 2013 Pivotal. All rights reserved. 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)
  54. 54. 54© Copyright 2013 Pivotal. All rights reserved. Predictive Maintenance for Drilling @Rashmi Raghu, Kaushik Das, Niels Kasch
  55. 55. 55© Copyright 2013 Pivotal. All rights reserved. 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
  56. 56. 56© Copyright 2013 Pivotal. All rights reserved. How are models built using sensor data? Integrating & Cleansing Feature Building Modeling
  57. 57. 57© Copyright 2013 Pivotal. All rights reserved. How are models built using sensor data? 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
  58. 58. 58© Copyright 2013 Pivotal. All rights reserved. How are models built using sensor data? Primary data sources ROP Time 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) Integrating & Cleansing
  59. 59. 59© Copyright 2013 Pivotal. All rights reserved. How are models built using sensor data? ROP Time Drill equipment changes Operational changes Substrate changes Primary data sources 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) Integrating & Cleansing
  60. 60. 60© Copyright 2013 Pivotal. All rights reserved. How are models built using sensor data? 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  61. 61. 61© Copyright 2013 Pivotal. All rights reserved. How are models built using sensor data? WOB Time Primary data sources 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) Integrating & Cleansing
  62. 62. 62© Copyright 2013 Pivotal. All rights reserved. How are models built using sensor data? 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details Drill Rig Sensor Data ( billions of records ) • Rate of Penetration (ROP) • RPM • Weight on Bit (WOB) Integrating & Cleansing

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