2.2.2 computational methods

• Problem decomposition breaks down problems into sub problems
• Decomposition makes the project easier to manage as different sub problems can be assigned to other people.
• abstraction can reduce memeory requirements
• abstraction can simplify the problems being solved
• Caching can be used in different abstraction simulators to store data from the abstraction into the cache, in case it is needed again
• Data mining is a technique used to identify patterns or outliers in large sets of data, termed big data.
• Data mining can spot trends or identify correlations between data which are not immediately obvious.
• Insights from data mining can be used to make predictions about the future based on previous trends. This makes data mining a useful tool in assisting business and marketing decisions.
• Heuristics are a non-optimal, ‘rule-of-thumb’ approach to problem-solving which are used to find an approximate solution. It is not a perfect solution but an optimal one
• Heuristics are used to provide an estimated solution for intractable problems such as the renowned Travelling Salesman Problem as well as the A* algorithm
• Performance modelling eliminates the need for true performance testing by providing mathematical methods to test a variety of loads on different operating systems.
• Performance modelling provides a cheaper, less time-consuming or safer method of testing applications as well as capabilities to predict performance
• Performance modelling can be used in delivery systems to test with large or small values. Example being the largest number of deliveries that can be made.
• Performance modelling can be used to simulate the behaviour of a system under different conditions.
• Pipelining is a process that allows for projects to be delivered faster, as modules are divided into individual tasks, with different tasks being developed simultaneously.
• Visualisation is where data can be presented in a way that is easier for us to understand using visualisation. This makes it possible to identify trends that were not otherwise obvious, particularly amongst statistical data.
• Visualisation can be represented as graphs, trees or charts.