Adaptive Filters for Continuous Queries over Distributed Data Streams
Chris Olston, Jing Jiang, and Jennifer Widom
Abstract
We consider an environment where distributed data sources continuously
stream updates to a centralized processor that monitors continuous
queries over the distributed data. Significant communication overhead
is incurred in the presence of rapid update streams, and we propose a
new technique for reducing the overhead. Users register continuous
queries with precision requirements at the central stream processor, which
installs filters at remote data sources. The filters adapt to
changing conditions to minimize stream rates while guaranteeing that
all continuous queries still receive the updates necessary to provide
answers of adequate precision at all times. Our approach enables
applications to trade precision for communication overhead at a fine
granularity by individually adjusting the precision constraints of
continuous queries over streams in a multi-query workload. Through
experiments performed on synthetic data simulations and a real
network monitoring implementation, we demonstrate the effectiveness of
our approach in achieving low communication overhead compared with
alternate approaches.
Conference Paper (SIGMOD 2003): [PS], [PDF].
Citation: [BibTeX]
Extended Version: [PS], [PDF]
TRAPP Project Web Page: [HTML]
STREAM Project Web Page: [HTML]