Identify

solve problems where we are given a set of elements to divide them into two parts
scheduling/finding the earliest or latest meeting at any given time
finding the median in a large collection (median is the middle value in an ordered integer list, if the size of the list is even, there is no middle value and the median is the mean of the two middle values)
searching for the largest and smallest values in a set
leveraging in order to implement a priority queue and other data structures
sometimes, useful in problems featuring a binary tree data structure

Motive

interested in knowing the smallest element in one part and the biggest element in the other

use two heaps:
a Min Heap to find the smallest element
a Max Heap to find the biggest element
pattern works by
- storing the first half of numbers in a Max Heap, this is because you want to find the largest number in the first half
- then store the second half of the numbers in a Min Heap, as you want to find the smallest number in the second half
- at any time, the median of the current list of numbers can be calculated from the top element of the two heaps
- inserting a number in a heap will take O(log n) (better than the brute force approach inserting a number in a sorted list)