7.2 Computational Methods (OCR A Level Computer Science) Flashcards

Not all problems should or can be solved with software.

False.

Not all problems should or can be solved with software; some may require other types of solutions.

The primary issue was the slow response time in the online appointment booking system, leading to user frustration and abandoned bookings.

A key sub problem in the GP surgery booking system was peak hour bottlenecks during peak hours.

Because problems like low morale, unrealistic targets, and lack of emotional support are cultural and psychological, which require human-centric solutions rather than just software.

In the sales team example, low morale and high turnover were the core issues affecting company performance.

Decomposition is the process of breaking down a big problem into smaller problems so that they can be solved independently.

Programmers use decomposition to break problems down and identify the steps involved.

Decomposition is useful because it allows programmers to break complex problems into smaller, more manageable parts, which can be solved independently or by different people.

Applying abstraction helps remove non-essential elements so programmers can focus on critical aspects of a problem.

First, identify sub-problems such as server limitations, inefficient algorithms, and poor user interface. Then, prioritise which to solve first based on their impact.

Abstraction is the process of removing non-essential details so that programmers can focus on the most important aspects of a problem.

True.

Decomposition helps programmers find parts of a problem that can be reused in other solutions or projects.

Software solutions such as improved algorithms, better data quality, and user feedback can help, but software alone cannot account for all real-world variables or rapid changes in customer behaviour.

The divide and conquer strategy is a method of solving complex tasks by breaking them down into smaller, more manageable sub-tasks, solving each one, and then combining their solutions.

The three main steps are: divide the problem into sub-problems, conquer by solving the sub-problems, and combine the solutions to form the overall answer.

In the divide and conquer strategy, the problem is broken into smaller tasks, solved independently, and their solutions are combined.

False.

Divide and conquer can make programs more time efficient by breaking complex problems into manageable sub-problems.

Task parallelism is when several tasks or sub-tasks can be carried out concurrently (at the same time) to speed up the overall completion time.

Task parallelism allows different teams or machines to assemble different components of a product at the same time, speeding up overall production.

A benefit of divide and conquer is that it can make efficient use of cache memory, but a drawback is the risk of stack overflows if recursion is used.

The search problem can be divided into tasks like query parsing, data retrieval, and front-end rendering. Task parallelism can then be used to process database searches and display products simultaneously, improving speed and user experience.

Abstraction is the removal of unnecessary components of a problem to focus only on those that are necessary.

Abstraction is important because it allows us to ignore irrelevant details and concentrate on the essential aspects of a problem, making it easier to solve complex real world problems.

In computer games, abstraction is used to hide unnecessary details, such as the complex algorithms controlling non-player characters, so players can focus on gameplay and enjoyment.

When using abstraction in a computer game, the aim is to make the game realistic and visually appealing, while keeping it fun to play.

Abstraction in recipes allows the user to focus on the steps needed to achieve the desired dish, without needing to understand the underlying science or chemistry involved.

In a recipe, a user does not need to know the chemistry behind the process, only the desired output.

Abstraction allows drivers to use simple controls like pedals and a key without needing to know how the engine works, enabling them to operate complex machines easily.

Abstraction empowers people to utilise complex machines like cars.

Backtracking is an algorithmic technique that incrementally builds possible solutions and abandons them if they fail, returning to previous decision points to try alternative paths.

Backtracking is ideal for solving logic problems, such as puzzles and mazes, where all possible paths need to be explored to find a solution.

Backtracking will explore all possible paths in the search space to determine if these come to dead ends.

True.

Backtracking will comprehensively explore all possible paths and is guaranteed to find a solution if one exists in the search space.

Backtracking starts at the entrance and moves step-by-step through the maze, marking visited paths. If it encounters a dead end, it retreats to the last unvisited junction and tries a new path until it reaches the exit.

A dead end in backtracking refers to a point in the search space where no valid moves or extensions are possible, requiring the algorithm to backtrack.

Backtracking can be used to traverse data structures such as trees or graphs which have multiple paths to the desired solution.

One major drawback of backtracking is high time complexity, especially for problems with a large search space, which makes it inefficient and can consume a lot of memory.

Data mining is the process of turning large quantities of data into useful information by using algorithms and statistical methods to find patterns and insights.

Organisations use data mining to find patterns and trends that are not immediately obvious, helping them make better predictions and decisions.

False.

Data mining is used in many industries such as retail, healthcare, finance, automotive, and entertainment.

Data mining uses algorithms and statistical methods to extract valuable insights from large data sets.

Online retailers like Amazon use data mining to analyse purchase history and browsing behaviour to provide customised product suggestions for customers.

If data mining uses inaccurate data, it can produce inaccurate results.

One complexity of data mining is the need for data engineers and data scientists staff within an organisation.

In the healthcare industry, data mining is used to analyse healthcare records and predict disease outbreaks or patient admissions.

In A Level Computer Science, heuristics is making use of experience, such as rules of thumb or educated guesses, to find a solution to a problem more quickly, prioritising speed over accuracy.

The main goal when using heuristics is to find a solution that is ‘good enough’ as quickly as possible, rather than guaranteeing the most accurate or perfect answer.

Heuristics often prioritises speed over accuracy.

False.

Heuristics aims for a solution that is good enough and does not guarantee the best or most accurate answer.

In 'Hot and Cold', the feedback 'hotter' or 'colder' tells the searcher if they are getting closer to or further from the hidden object, similar to how heuristics provide guidance but not an exact answer.

Getting stuck in a spot that seems 'hot' but is not the actual target is known as reaching a local optimum.

The A* algorithm uses heuristics to estimate the best path from a start node to an end node, allowing it to find a solution quickly, though not always the most efficient path.

One benefit of using heuristics is that they save time by not investigating every possibility.

Performance modelling is a systematic approach to testing or simulating the behaviour of a system before it is used in the real world.

The main purpose of performance modelling is to evaluate and predict the performance characteristics of a software system before it is deployed.

Performance modelling uses metrics like response time and throughput to identify potential bottlenecks.

True.

Performance modelling can be used in the design phase to make architectural decisions and in the testing phase to simulate real-world scenarios.

Stress testing is a process where a system is tested under extreme data loads or large numbers of users to ensure it can cope with such conditions.

If the rules that made up a performance model are wrong, the results produced will be incorrect.

Performance modelling can simulate different database architectures and query strategies, helping to select the best indexing strategy and estimate response times under varying loads.

Performance modelling is important for battery-powered devices because it can estimate power consumption under different usage patterns, helping to optimise energy efficiency.

Pipelining is the process of carrying out multiple instructions concurrently, where the output of one process feeds into the next to improve efficiency and performance.

To utilise pipelining, you must break down the task, arrange tasks in sequential order, and allow multiple tasks to operate concurrently.

Pipelining in programming improves efficiency by chaining multiple instructions together so that the output of one operation becomes the input of the next, similar to an assembly line.

In Unix, the pipe (|) symbol is used to chain commands together, passing the output of one command to the input of the next.

Pipelining is similar to an assembly line because tasks are broken down and arranged in sequence, enabling different stages to operate at the same time and increasing efficiency.

When applying pipelining to manufacturing a car, tasks that can be performed concurrently should be identified, such as developing the engine and chassis at the same time.

False.

In pipelining, multiple tasks operate concurrently, not sequentially one after the other.

The pipe (|) symbol in Unix is used to pass the output of one command as input to another, enabling pipelining of commands.

Visualisation is when data or concepts are presented in a simpler form for humans to understand, often by creating a graphical or visual representation.

The main benefit of using visualisation is that it simplifies complex data or systems, making them easier for humans to understand and helping to spot new trends and patterns.

False.

Visualisation can show data in a visual way, but it cannot explain the reasons behind why something is the way it is.

A flowchart is a diagram that represents workflows or processes in a system.

Wireframes help with layout planning and user experience design, allowing designers to visualise the skeletal framework of a program or website before detailed development.

A Unified Modeling Language (UML) diagram is a standard way to visualise a system's architecture, showing how classes and objects interact with each other.

Visualisation can make it easier to spot trends and patterns in data that have not been noticed before.

Different people may interpret the same visualisation differently because the way data is presented can influence understanding and individuals may focus on different aspects of the visual information.