Answer:
O(N!), O(2N), O(N2), O(N), O(logN)
Explanation:
N! grows faster than any exponential functions, leave alone polynomials and logarithm. so O( N! ) would be slowest.
2^N would be bigger than N². Any exponential functions are slower than polynomial. So O( 2^N ) is next slowest.
Rest of them should be easier.
N² is slower than N and N is slower than logN as you can check in a graphing calculator.
NOTE: It is just nitpick but big-Oh is not necessary about speed / running time ( many programmers treat it like that anyway ) but rather how the time taken for an algorithm increase as the size of the input increases. Subtle difference.
It really depends on your type of data, if it's sensitive data or not. And it also depends on what type of backup (Incremental, Decremental, etc.). But you should aim to backup at least once a week, and also do a backup of your original backup.
<span>It is a logical, systematic search for the source of a problem in order to solve it, and make the product or process operational again.Troubleshooting is needed to identify the symptoms. ...Troubleshooting is the process of isolating the specific cause or causes of the symptom.</span>
The item that you would most likely to keep in a database is a Payroll record. Payroll records are numbers and inputs/outputs of employees of a certain company. Numbers are easier to manipulate and easier to manage than statements, letters and addresses that are basically letters.
