Applying OLAP Pre-Aggregation Techniques to ... - Jacobs University

Chapter 4
Answering Basic Aggregate Queries
Using Pre-Aggregated Data
As discussed in previous chapters, aggregation is an important mechanism that allows
users to extract general characterizations from very large repositories of data. In this
chapter, we study the effect of selecting a set of aggregate queries, compute their
results and use them for subsequent query requests. In particular, we study the effect
of pre-aggregation in computing aggregate queries in the field of GIS and remotesensing
imaging applications.
We introduce a pre-aggregation framework that distinguishes among different types
of pre-aggregates for computing a query. We show that in most cases, several preaggregates
may qualify for answering an aggregate query and address the problem of
selecting the best pre-aggregate in terms of execution time. To this end, we introduce
a model that measures the cost of using qualified pre-aggregates for the computation
of a query. We then present an algorithm that selects the best pre-aggregate for computing
a query. We measure the performance of our algorithms in an array database
management system (RasDaMan), and show that our algorithms give much better performance
over straightforward methods.
4.1 Framework
Most major database management systems allow the user to store query results
through a process known as view materialization. The query optimizer may then automatically
use the materialized data to speed up the evaluation of a new query. Queries
that benefit from using materialized data are those that involve the summarization of
large amounts of data. They are known as aggregate queries because their query statements
include one or more aggregate functions. The ANSI SQL:2008 standard defines
a wide variety of aggregate functions including: COUNT, SUM, AVG, MAX, MIN,
EVERY, ANY, SOME, VAR POP, VAR SAMP, STDDEV POP, STDDEV SAMP, AR-
RAY AGG, REGR COUNT, COVAR POP, COVAR SAMP, CORR, REGR R2, REGR SLOPE,
and REGR INTER-CEPT [20].
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