The probabilistic stream model was introduced by Jayram et al.[2007]. It is a generalization
of the data stream model that is suited to handling probabilistic data, where each item of the …

In many stream monitoring situations, the data arrival rate is so high that it is not even
possible to observe each element of the stream. The most common solution is to sample a …

The management of uncertain, probabilistic data has recently emerged as a useful paradigm
for dealing with the inherent unreliabilities of several real-world application domains …

A fundamental problem in data management and analysis is to generate descriptions of the
distribution of data. It is most common to give such descriptions in terms of the cumulative …

Capturing characteristics of large data streams has received considerable attention. The
constraints in space and time restrict the data stream processing to only one pass (or a small …

Recent years have witnessed an increasing interest in designing algorithms for querying
and analyzing streaming data (ie, data that is seen only once in a fixed order) with only …

Data streams characterize the high speed and large volume input of a new class of
applications such as network monitoring, web content analysis and sensor networks. Among …

In many applications from telephone fraud detection to network management, data arrives in
a stream, and there is a need to maintain a variety of statistical summary information about a …

We study the problem of computing aggregation operators on probabilistic data in an I/O
efficient manner. Algorithms for aggregation operators such as SUM, COUNT, AVG, and …

Complex queries over high speed data streams often need to rely on approximations to
keep up with their input. The research community has developed a rich literature on …