  High-volume, high-speed data streams may overwhelm the capabilities of stream
processing systems; techniques such as data prioritization, avoidance of
unnecessary processing and on-demand result production may be necessary to
reduce processing requirements. However, the dynamic nature of data streams, in
terms of both rate and content, makes the application of such techniques
challenging. Such techniques have been addressed in the context of static and
centralized query optimization; however, they have not been fully addressed for
data stream management systems. In this work, we present a comprehensive
framework that supports prioritization, avoidance of unnecessary work, and
on-demand result production over distributed, unreliable, bursty, disordered
data sources, typical of many data streams. We propose a form of inter-operator
feedback, which flows against the stream direction, to communicate the
information needed to enable execution of these techniques. This feedback
leverages punctuations to describe the subsets of interest. We identify
potential sources of feedback information, characterize new types of
punctuation to support feedback, and describe the roles of producers,
exploiters, and relayers of feedback that query operators may implement. We
present initial experimental observations using the NiagaraST data-stream
system.
