This document summarizes a research paper that proposes a new algorithm called ESW-FI to efficiently mine frequent itemsets from data streams using a sliding window model. The algorithm actively maintains potentially frequent itemsets in a compact data structure using only a single pass over the data. It guarantees output quality and bounds memory usage. The algorithm divides the sliding window into fixed-size segments and processes window slides by inserting new segments and removing old ones, avoiding reprocessing of all transactions on each slide.