Random sampling is an appealing approach to build synopses of large data streams because random samples can be used for a broad spectrum of analytical tasks. Users are often inter...
APPEARED IN ACM PODS-2009. A sliding windows model is an important case of the streaming model, where only the most "recent" elements remain active and the rest are disc...
Vladimir Braverman, Rafail Ostrovsky, Carlo Zaniol...
We consider the problem of maintaining approximate counts and quantiles over fixed- and variablesize sliding windows in limited space. For quantiles, we present deterministic algo...
Abstract. We study the problem of maintaining a (1+ )-factor approximation of the diameter of a stream of points under the sliding window model. In one dimension, we give a simple ...
We consider the problem of maintaining aggregates over recent elements of a massive data stream. Motivated by applications involving network data, we consider asynchronous data str...