  Analyzing patterns in a sequence of events has applications in text analysis,
computer programming, and genomics research. In this paper, we consider the
all-window-length analysis model which analyzes a sequence of events with
respect to windows of all lengths. We study the exact co-occurrence counting
problem for the all-window-length analysis model. Our first algorithm is an
offline algorithm that counts all-window-length co-occurrences by performing
multiple passes over a sequence and computing single-window-length
co-occurrences. This algorithm has the time complexity $O(n)$ for each window
length and thus a total complexity of $O(n^2)$ and the space complexity
$O(|I|)$ for a sequence of size n and an itemset of size $|I|$. We propose
AWLCO, an online algorithm that computes all-window-length co-occurrences in a
single pass with the expected time complexity of $O(n)$ and space complexity of
$O( \sqrt{ n|I| })$. Following this, we generalize our use case to patterns in
which we propose an algorithm that computes all-window-length co-occurrence
with expected time complexity $O(n|I|)$ and space complexity $O( \sqrt{n|I|} +
e_{max}|I|)$, where $e_{max}$ is the length of the largest pattern.
