1. Lesson Objectives Aims From the spec:
Learners should apply their knowledge of:
Backtracking
Data Mining
Heuristics
Performance Modelling
Pipelining
Visualisation
Thinking abstractly

2. This lesson
This will take a couple of lessons
It is exam question heavy
Some questions you should be able to answer from what you’ve done so far – computational thinking

3. Backtracking
An algorithmic approach to problem solving
A set of rules have been defined (the algorithm) and these form a path
The path is followed, if a rule fails on a piece of data, then the algorithm returns to the last known good point

4. Data Mining
“Big data” is a big problem and massive opportunity at the same time
Modern systems enable us to collect and analyse extraordinary amounts of data
Usually from multiple sources
Great examples are google, facebook and twitter – why?

5. Data mining can be used to find relationships between seemingly unrelated data
Applies to data with dissimilar structures
May throw up unexpected correlations

6. Applications
Supermarkets
Insurance companies
Business Modelling
Disease pattern modelling

7. Methodology The method of searching through seemingly unconnected data
Methods are “pattern matching” and “anomaly detection”
May include some method of correlation calculation
Does not need a pre-determined “matching criteria” – it’s job is to find what matches!
Only possible because of modern processing power on high speed machines (a brute force approach)

8. Exam Questions – Data mining

10. Heuristics
Many problems in computer science are either unsolvable or would take too long to solve in a sensible amount of time.
Sat Nav is a classic example – how do you know you’re taking the best, fastest or shortest route without analysing ALL roads?

11. Heuristics provide a solution by providing a “best fit” or “good enough” answer.
This will solve problems much more quickly than an “exact” or brute force
Can also find an approximate solution when there is no exact solution available

12. Heuristics are a trade off:
Optimality
Completeness
Accuracy
Precision
The outcome of these trade offs are:
Speed
A solution that is “good enough”

13. Solving a problem “heuristically”
Gather all relevant data, but not necessarily ALL data
Data gathered should be the most likely to help in the given situation
Make judgements based on rules or previous experiences

14. Example – Virus scanning
Needs to happen quickly
Not all viruses are know about!
Looks for code or behaviour that is similar to known viruses (classes)
Has different rules depending on virus type or family
If patterns match or are similar then it infers a virus
Can work for new/unidentified viruses

15. Exam Questions - Heuristics

17. 9 Marks

20. We’ve seen this in CPU’s
Pipelining
We’ve seen this in CPU’s
The output of one task is the input of another
Enables jobs to be queued and also run in parallel to increase productivity or throughput
Used in:
CPU (RISC especially)
Command line systems (Linux Pipe command)
Task Scheduling

21. In real use… To pipeline Identify processes that MUST run in sequence
Identify processes that may run in parallel
Identify when processes must converge (“obligatory sequence”)
What must happen before another process can start? E.g. Underwear must be on before trousers.

22. Exam Question - Pipelining

24. Answers

25. Performance Modelling
Modelling or simulating real world objects or situations
For the purpose of:
Finding out effectiveness
Tweaking and changing parameters to observe their effects
Safety
Experimentation
Finding optimal solutions

26. Simply converting raw data into a visual form. Examples:
Visualisation
Simply converting raw data into a visual form.
Examples:
Graphs
Heat Maps
Mapping (in general)
Touch maps (football?)
A much more powerful method of interpreting data

27. Question 2 – Comp. thinking

29. Answers

30. Exam Questions - Abstraction

35. Review/Success Criteria
You should know
Lots of things.