Supporting Top-k aggregate queries over unequal synopsis on Internet traffic streams

Queries that return a list of frequently occurring items are important in the analysis of real-time Internet packet streams. While several results exist for computing Top-k queries using limited memory in the infinite stream model (e.g., limited-memory sliding windows). To compute the statistics over a sliding window, a synopsis data structure can be maintained for the stream to compute the statistics rapidly. Usually, a Top-k query is always processed over an equal synopsis, but it's very hard to implement over an unequal synopsis because of the resulting inaccurate approximate answers. Therefore, in this paper, we focus on periodically refreshed Top-k queries over sliding windows on Internet traffic streams; we present a deterministic DSW (Dynamic Sub-Window) algorithm to support the processing of Top-k aggregate queries over an unequal synopsis and guarantee the accuracy of the approximation results.