  Streaming computation plays an important role in large-scale data analysis.
The sliding window model is a model of streaming computation which also
captures the recency of the data. In this model, data arrives one item at a
time, but only the latest $W$ data items are considered for a particular
problem. The goal is to output a good solution at the end of the stream by
maintaining a small summary during the stream.
  In this work, we propose a new algorithmic framework for designing efficient
sliding window algorithms via bucketing-based sketches. Based on this new
framework, we develop space-efficient sliding window algorithms for $k$-cover,
$k$-clustering and diversity maximization problems. For each of the above
problems, our algorithm achieves $(1\pm \varepsilon)$-approximation. Compared
with the previous work, it improves both the approximation ratio and the space.
