Cost Models for Query Processing Strategies in the Active Data
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Cost Models for Query Processing Strategies in the Active Data
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Date
1999-10-13
Authors
Chang, Chialin
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Abstract
Exploring and analyzing large volumes of data plays an increasingly
important role in many domains of scientific research. We have been
developing the Active Data Repository (ADR), an infrastructure that
integrates storage, retrieval, and processing of large multi-dimensional
scientific datasets on distributed memory parallel machines with multiple
disks attached to each node. In earlier work, we proposed three strategies
for processing range queries within the ADR framework. Our experimental
results show that the relative performance of the strategies changes under
varying application characteristics and machine configurations. In this
work we describe analytical models to predict the average computation, I/O
and communication operation counts of the strategies when input data
elements are uniformly distributed in the attribute space of the output
dataset, restricting the output dataset to be a regular d-dimensional
array. We validate these models for various synthetic datasets and for
several driving applications.
Also cross-referenced as UMIACS-TR-99-54