Clustering on Sliding Windows in Polylogarithmic Space

In PODS 2003, Babcock, Datar, Motwani and O’Callaghan [4] gave the first streaming solution for the k-median problem on sliding windows using O( k τ4W 2τ log2 W ) space, with a O(2O(1/τ)) approximation factor, where W is the window size and τ ∈ (0, 2 ) is a user-specified parameter. They left as an open question whether it is possible to improve this to polylogarithmic space. Despite much progress on clustering and sliding windows, this question has remained open for more than a decade. In this paper, we partially answer the main open question posed by Babcock, Datar, Motwani and O’Callaghan. We present an algorithm yielding an exponential improvement in space compared to the previous result given in Babcock, et al. In particular, we give the first polylogarithmic space (α, β)-approximation for metric k-median clustering in the sliding window model, where α and β are constants, under the assumption, also made by Babcock et al., that the optimal k-median cost on any given window is bounded by a polynomial in the window size. We justify this assumption by showing that when the cost is exponential in the window size, no sublinear space approximation is possible. Our main technical contribution is a simple but elegant extension of smooth functions as introduced by Braverman and Ostrovsky [9], which allows us to apply well-known techniques for solving problems in the sliding window model to functions that are not smooth, such as the k-median cost. 1998 ACM Subject Classification I.5.3 Clustering