This document discusses big data and functional programming. It defines big data in terms of volume, velocity, and variety of data. It provides examples of big data analysis in healthcare, Google, and Amazon. It then explains that functional programming is suitable for big data due to its lack of side effects, statelessness, support for higher-order functions, and forbidding of assignment, which allows for parallel processing. Finally, it describes the fact-based model for representing changing data over time and graph schemas for capturing relationships between data.

1. AQA
A Level
Computer Science
T7c | Big Data
T7
Functional Paradigm

2. Objectives
• Explain the concept of Big Data
• Describe examples of Big Data
• Explain the fact-based model for representing data
• Explain the graph schema for capturing structure
• Analyse the features of functional programming that
make it suitable for Big Data

3. Big Data
• Volume
• too big for a single server
• Velocity
• milliseconds or seconds to respond
• Variety
• many forms, structured/unstructured/text/multimedia

4. Big Data
• Does mobile use increase likelihood of cancer?
• Six billion mobile phones in the world, lots of data to analyse
• How can you improve voice-translation software?
• Score the probability that a snippet corresponds to a specific
word
• How does the bank of England check if house prices
are rising or falling?
• Analyse search queries on property

5. Healthcare
• Retrospective data (prior)
• Real-time data (blood pressure, temperature, etc)
• We can now consider gene sequencing but its
estimated a human body contains 150 trillion
gigabytes of information…

6. Google
• Process more than 24 petabytes of data every day
• >3 billion search queries every day (and each one is
saved)
• Between 2003 – 2008, this data was used to see
which areas of the US was affected by seasonal flu

7. Amazon
• Recommends books based on purchases
• Originally done by similar topics/authors
• However, if you bought a book on Python, you might
not need another one
• Now they apply Big Data techniques to analyse
purchases e.g. huge numbers of customers bought
both Dan Brown and Ian McEwan books, therefore
one author recommends the other

8. Functional Programming
• No side effects
• They don’t modify global variables
• Statelessness – not affected by how often a function is called
or the order
• Support higher order functions
• One or more functions as an input
• Outputs a function
• Forbid assignment
• Makes parallel processing across server easy as each will
always give the same result

9. Fact-base Model
• Facts are recorded with timestamp
• Continues to grow
• Changes (e.g. surname) are added as another fact
so history is always there

10. Graph Schema

11. Graph Schema

12. Objectives
• Explain the concept of Big Data
• Describe examples of Big Data
• Explain the fact-based model for representing data
• Explain the graph schema for capturing structure
• Analyse the features of functional programming that
make it suitable for Big Data