bottomtiltopn

Bottomtiltopn is a term that appears in informal discussions of data processing and algorithm design. It is not established as a standard concept with a formal definition in major reference works, and its meaning can vary by context. In many usages, it denotes a pattern of extracting elements by starting from the bottom of a collection and moving upward, up to n elements.

One common interpretation is bottom-N selection in a sorted sequence: bottomtiltopn(A, n) returns the n smallest elements of A. A related reading applies to unsorted data, where the operation first identifies the n smallest items and then returns them (often in ascending order). The phrase is also used more generally to describe focusing on lower-end items or outcomes up to a maximum of n, such as in reporting, prioritization, or visualization.

From an implementation perspective, bottomtiltopn can be achieved with partial sorting or selection algorithms. If only the n smallest elements are required, using a min-heap or a selection algorithm yields time complexity on the order of O(m log n) for a list of length m, or O(m + n log m) depending on the approach. If the entire list must be fully sorted, the cost is O(m log m).

Example: for the list [7, 3, 9, 1, 5] and n = 3, bottomtiltopn may yield [1, 3, 5] (the three smallest elements in ascending order). The exact ordering and inclusion of duplicates depend on the chosen interpretation.

See also: bottom-k problem, top-N, partial sort, selection algorithm.