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 …

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 …

IP packet streams consist of multiple interleaving IP flows. Statistical summaries of these
streams, collected for different measurement periods, are used for characterization of traffic …

StreaMon is the adaptive query processing engine of the STREAM prototype Data Stream
Management System (DSMS)[4]. A fundamental challenge in many DSMS applications (eg …

This volume focuses on the theory and practice of data stream management, and the novel
challenges this emerging domain poses for data-management algorithms, systems, and …

This paper describes our ongoing work developing the Stanford Stream Data Manager
(STREAM), a system for executing continuous queries over multiple continuous data …

Researchers in data management have recently recognized the importance of a new class
of data-intensive applications that requires managing data streams, ie, data composed of …

We consider the problem of pipelined filters, where a continuous stream of tuples is
processed by a set of commutative filters. Pipelined filters are common in stream …

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 …

Traditional Database Management Systems (DBMS) software is built on the concept of
persistent data sets, that are stored reliably in stable storage and queried/updated several …