Finding top-k elements in a time-sliding window

Identifying the top-k most frequent elements is one of the many problems associated with data streams analysis. It is a well-known and difficult problem, especially if the analysis is to be performed and maintained up to date in near real time. Analyzing data streams in time sliding window model is of particular interest as only the most recent, more relevant events are considered. Approximate answers are usually adequate when dealing with this problem. This paper presents a new and innovative algorithm, the Filtered Space-Saving with Sliding Window Algorithm (FSW) that addresses this problem by introducing in the Filtered Space Saving (FSS) algorithm an approximated time sliding window counter. The algorithm provides the top-k list of elements, their frequency and an error estimate for each frequency value within the sliding window. It provides strong guarantees on the results, depending on the elements real frequencies. Experimental results detail performance on real life cases.