Data Science in the Real World: Making

a Difference

Srinath PereraDirector Research WSO2, Apache Member

(@srinath_perera) srinath@wso2.com

StatDay 2015 @ University of Colombo

Outline Making sense of World’s Data Building Data Systems Changing Dynamics of Data Analysis with Big Data ( Sensor Data)

Challenges and Open Problems

Michael Stonebraker“But then, out of nowhere, some marketing guys started talking about ‘big data, That’s when I realized that I’d been studying this thing for the better part of my academic life.”

Michael Stonebraker“But then, out of nowhere, some marketing guys started talking about ‘big data, That’s when I realized that I’d been studying this thing for the better part of my academic life.”

ACM Turing Award, 2015

A Day in Your LifeThink about a day in your life?- What is the best road to take?- Would there be any bad weather?- How to invest my money?- How is my health?

There are many decisions that you can do better if only you can access the data and process them.

http://www.flickr.com/photos/kcolwell/5512461652/ CC licence

What can We do with Data?Optimize (World is inefficient)- 30% food wasted farm to plate- GE Save 1% initiative (http://goo.gl/eYC0QE )

- Trains => 2B/ year- US healthcare => 20B/ year

Save lives - Weather, Disease identification, Personalized treatment

Technology advancement- Most high tech research are done via simulations

Building Data Processing Systems

Data Science Architecture

Data Processing Technologies Landscape

Batch ProcessingStore and process Slow (> 5 minutes for results for a reasonable usecase)

Programming model is MapReduce - Apache Hadoop- SparkLot of tools built on top - Hive Shark for (SQL style queries), Mahout (ML), Giraph

(Graph Processing)

Usecase: Big Data for development

Done using CDR dataPeople density noon vs. midnight (red => increased, blue => decreased)

Urban Planning - People distribution - Mobility - Waste Management- E.g. see http://goo.gl/jPujmM

From: http://lirneasia.net/2014/08/what-does-big-data-say-about-sri-lanka/

Value of some Insights degrade Fast!

For some usecases ( e.g. stock markets, traffic, surveillance, patient monitoring) the value of insights degrades very quickly with time. - E.g. stock markets and speed of lightWe need technology that can produce outputs fast - Static Queries, but need very

fast output (Alerts, Realtime control)

- Dynamic and Interactive Queries ( Data exploration)

Complex Event Processing

Predictive Analytics If we know how to solve a problem, that is if

we know a finite set of rules, then we can programs it.

For some problems (e.g. Drive a car, character recognition), we do not know a finite fix rule set.

Instead of programming, we give lot of examples and ask the computer to learn (often called Machine Learning)

Lot of tools - R ( Statistical language)- Sci-kit learn (Phython)- Apache Spark’s MLBase and Apache Mahout

(Java)

Usecase: Predictive Maintenance Idea is to fix the problem before it broke, avoiding expensive downtimes- Airplanes, turbines, windmills - Construction Equipment- Car, Golf carts

How- Build a model for normal operation

and compare deviation- Match against known error

patterns

Communicate: Dashboards

Idea is to given the “Overall idea” in a glance (e.g. car dashboard)

Support for personalization, you can build your own dashboard.

Also the entry point for Drill down

How to build?- Expose data via JSON- Build Dashboard via Google

Gadget and content via HTML5 + java scripts (Use charting libraries like Vega or D3)

Communicate: Alerts and Triggers

Detecting conditions can be done via Event Processing system ( e.g. CEP)

Key is the “Last Mile”- Email- SMS- Push notifications to a UI- Pager - Trigger physical Alarm

Case Study: Realtime Soccer Analysis

Watch at: https://www.youtube.com/watch?v=nRI6buQ0NOM

Changing Dynamics

Large Observational DatasetsStats are easy with designed experiments - You got to select a representative

set - You have a control group

You have lot and lot of data and lot and lot of computing power ( compared to what you had) Two reactions!!

“It is better to be roughly right than

precisely wrong.” ― John Keynes

In the long run, we are all Dead!!

Challenges: Causality Correlation does not imply Causality!! ( send

a book home example [1]) Causality

- do repeat experiment with identical test - If CAN’T do a randomized test (A/B test)- With Big data we cannot do either

Option 1: We can act on correlation if we can verify the guess or if correctness is not critical (Start Investigation, Check for a disease, Marketing )

Option 2: We verify correlations using A/B testing or propensity analysis

[1] http://www.freakonomics.com/2008/12/10/the-blagojevich-upside/[2] https://hbr.org/2014/03/when-to-act-on-a-correlation-and-when-not-to/

Curious Case of Missing Data

http://www.fastcodesign.com/1671172/how-a-story-from-world-war-ii-shapes-facebook-today, Pic from http://www.phibetaiota.net/2011/09/defdog-the-importance-of-selection-bias-in-statistics/

•WW II, Returned Aircrafts and data on where they were hit?•How would you add Armour?

More Data Beat a Clever Algorithm

Observed by large internet companies

Also seen over keggle Competitions

E.g. SVM vs. Logistic regression

Read “A Few Useful Things to Know about Machine Learning” (Pedro Domingos)

Challenges: Feature Engineering

In ML feature engineering is the key [1]. You need features to form a kernel. Then you can solve with less data.

Deep learning can learn best feature (combination) via semi or unsupervised learning [2]1. Bekkerman’s talk https://www.youtube.com/watch?v=wjTJVhmu1JM

2. Deep Learning, http://cl.naist.jp/~kevinduh/a/deep2014/

Challenges: Taking Decisions (Context)

Challenges: Updating Models● Incorporate more data

o We get more data over time o We get feed back about

effectiveness of decisions (e.g. Accuracy of Fraud)

o Trends change● Track and update model

o Generate models in batch mode and update

o Streaming (Online) ML, which is an active research topic

Challenges: Lack of Labeled Data•Most data is not labeled •Idea of Semi Supervised learning •Provide Data + Examples + Ontology, and algorithm find new patterns –Lot of Data –Few example sentences •Often uses Expectations Maximization (EM) Algorithm Watch Tom Mitchell’s Lecture https://www.youtube.com/watch?

v=psFnHkIjHA0

Ontology: People, Cities

Relationships: like,

dislike, live in

Examples: Bob (People) lives in Colombo (City)

Two TakeawaysDo your data Processing as part of a Bigger system - Think Systems, automate, make a difference - Realtime vs Batch - Use tools ( Do not reinvent the wheel)

Think how dynamics are changing (Uncontrolled experiments, lot of Data) - Do not be a data Pessimist - However, do not do stupid things either

Questions?