2.2 Computational Methods

marketing campains improving reaction cyber security issues big data huge amountd of data are processed look through vast amounts of data

data mining involves searching through unconnected data to find a correlation

problems which take a series of decisions and if there is not enough information to know which decision is best

searching trees solving sudokus and crosswords

depth first traversal

take one route as far as possible pushing every node onto the stack and once reaching a leaf node popping it off the stack and checking the node then back tracks up popping nodes off until it reaches a node with an unchecked child node and the process is repeated

can improve marketing can increase sales

need a very powerful computer privacy concerns as the big data searched through must follow the GDPR (2018) innacurate data can produce false reports

an approach that uses a rule of thumb to find a good enough solution

simulating different user and system loads computer uses mathematical approximation can be done on cars so no need to destroy a real one simulates the behaviour of the system before it is used testing

saves money overall safer saves time in the long run

process that allows for multiple projects to be delivered modules are divided up into individual tasks tasks can be developed in parallel output of one module becomes the input to the next one (decomposition into functions and subroutines with each subroutine leading into the next like the network stack)

problems can be easier to solve with visual representation large datasets can be confusing i.e a 2d array being visualised as a table

benefits humans presents information in a simple to understand way best way to explain complex situations