"Processing Complex Aggregate Queries over Data Streams"
Recent years have witnessed an increasing interest in designing algorithms
for querying and analyzing streaming data (i.e., data that is seen only once
in a fixed order) with only limited memory.
Providing (perhaps approximate) answers to queries over such continuous data
streams is a crucial requirement for many application environments; examples include
large telecom and IP network installations where performance data from different
parts of the network needs to be continuously collected and analyzed.
In this paper,
we consider the problem of approximately answering general aggregate SQL queries over
continuous data streams with limited memory. Our method relies on randomizing techniques
that compute small "sketch" summaries of the streams that can then be used to provide
approximate answers to aggregate queries
with provable guarantees on the approximation error. We also
demonstrate how existing statistical information on the base data
(e.g., histograms) can be used in the proposed framework to improve
the quality of the approximation provided by our algorithms.
The key idea is to intelligently partition the domain of the underlying
attribute(s) and, thus, decompose the sketching problem in a way that
provably tightens our guarantees.
Results of our experimental study with real-life as well as synthetic data
streams indicate that sketches provide significantly more accurate
answers compared to histograms for aggregate queries. This is especially
true when our domain partitioning methods are employed to
further boost the accuracy of the final estimates.
Alin's talk slides
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