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computing - a level ocr (paper 2) > Computational Methods > Flashcards

Computational Methods Flashcards

1
Q

What is problem decomposition?

A
  • the breaking down of a problem into smaller parts that are easier to solve
  • the smaller parts can sometimes be solved recursively (run again and again until that part of the problem is solved)
  • different parts of the problem can be assigned to different programmers making use of their individual skills and lightening the load on them
  • can be a hierarchical approach (tree flow) or take into account Parallel processes
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2
Q

What is the problem with decomposition?

A
  • assumes that the whole solution to the problem is knowable in advance
  • less useful in more modern applications
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3
Q

What is a data flow diagram?

A
  • break down a problem into processes, data stores and data flows which leads to data flow diagrams
  • the major components and activities in a system are laid out before any effort is expended on the finer details
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4
Q

What is structured programming?

A
  • sequence, selection and iteration

- another common method of decomposition

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5
Q

1) What is one way to test if a problem is computable?
2) what is computability?
3) What are some of the limiting factors to the problems we can solve with computers?

A

1) Test it against the capabilities of a Turing machine
2) whether or not a problem can be solved using an algorithm. There can be no computable solution to some problems. Can combine computed solutions with human insight to get a solution
3) speed they run at and the memory they can access

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6
Q

What is a messy problem?

A
  • not all problems can be neatly described
  • this is because; underlying issues are not understood, data is not sufficient, data is erroneous or the underlying issues are very complex
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7
Q

What is abstraction?

A

Turning real world problems into a reality that can be processed. Also requires decomposition

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8
Q

What is backtracking?

A
  • trial and error
  • trying out a sequence of actions and determining how far they succeed until it becomes apparent that it can go no further
  • partial solutions are incrementally built up and if they fail these solutions are abandoned and the search begins again
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9
Q

What is problem recognition?

A
  • determine exactly what a problem is

- by using computational and intuitive methods it may be possible to come up with a solution

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10
Q

What is data mining?

A
  • examines large data sets looking for patterns and relationships
  • DB’s are designed to store and process data in predefined ways. When they get large enough, unexpected relationships might be uncovered
  • incorporates; cluster analysis, pattern matching, anomaly detection and regression analysis
  • useful for business modelling and disease prediction
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11
Q

What is performance modelling?

A
  • another use of models
  • real-life objects and systems, as well as computer software, can be modelled in order to predict how they will behave when in use
  • performance depends on complexity of algorithm
  • simulations can be used to predict performance
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12
Q

What is pipelining?

A
  • output of one process can be fed into another
  • useful in RISC processors
  • complex jobs can be divided up into separate pipelines so that parallel processing can occur
  • this parallels real-life situations such as assembly-line processing
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13
Q

What is visualisation?

A
  • problems and data can be better understood when translated into a visual model
  • computers have facilitated many new and intensive ways to visualise situations. These show unexpected trends that could not be produced by traditional methods
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14
Q

What is thinking abstractly?

A
  • Abstraction is a representation of reality
  • requires recognising what is important in a problem and what isnt. We only include the information that is required
  • we then devising a means to effectively code it
  • variables are an abstraction; they represent real world values in a calculation
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15
Q

What are levels of abstraction?

A
  • useful to construct an abstraction to represent a large problem and create lower-level abstractions to deal with component parts
  • details in each layer can be hidden from the others. Frees up solution process to concentrate on just one issue at a time
  • uses layers; divides up the functionality of a big system into separate areas of interest. Eg; car designer might be interested in properties of a new fuel but that issue is treated separately from the design of the dashboard
  • specialisation leads to reliability and cost benefits
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16
Q

What is a Heuristic approach?

A
  • Approach to problem solving that makes use of experience
  • not guaranteed to produce the best solution but it generally will produce a “good enough” result
  • referred to as “rule of thumb”
  • important to realise when “good enough is good enough” and when it isn’t.
17
Q

What is the first stage of problem solving?

A

Understanding the problem:

  • what are the known and unknowns
  • what data do we have and what data do we need. What data do we not need
  • is it solvable, can it be decomposed.
  • can we represent it abstractly using diagrams or variables
18
Q

What is the second stage of problem solving?

A

Devise a plan:

  • have you seen this problem or a similar one before
  • decompose the problem
  • make a to-do list
  • look for patterns
  • think outside the box (but stick to the brief)
  • is there a equation that can help
  • try solving a similar one if you are stuck
19
Q

What is the third stage of problem solving?

A

Carry out the plan:

  • Do this carefully, checking as you go
  • are you sure each stage is correct
  • dont be afraid to start again
20
Q

What is the fourth and final stage of problem solving?

A

Look back over solution:

  • can it be improved
  • might have overlooked bigger picture
  • have you learned something that could be applied to future problems
21
Q

1) What is caching? (RAM sense)

2) what is prefetching?

A

1) data might be stored in RAM ‘in case’ it is needed again before process shuts down
- if it is required, it does not need to be read again from dusk which gives it a faster response time with less latency
2) an instruction is requested from memory by the CPU before it is required to speed up instruction throughput
- there are algorithms that can predict likely future instructions needed so they are ready in the cache when they are in fact needed

22
Q

Positives and negatives of RAM caching?

A
  • reduces load on web severs because data required by an app can be anticipated, thereby reducing number of separate access actions.
  • can be very complicated to implement effectively
  • if wrong data is cached then it can be difficult to re-establish the correct sequence of data items or instructions
23
Q

What is another advantage of the divide and conquer approach?

A
  • separate modules of any other items such as data stores can be reused in future projects
  • this applies for things such as libraries.
  • Windows uses DLL (Dynamic link libraries). Called at runtime to provide certain functionality. Eg; don’t need to write code for a messagebox just link a DLL to produce it

computing - a level ocr (paper 2) (4 decks)

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