Information Technology Reference
In-Depth Information
•
The
Query Optimizer
examines all algebraic expressions that are
equivalent to the given query and chooses the one that is esti-
mated to be the cheapest.
The
•
Code Interpreter
transforms the access plan generated by the
optimizer into calls to the query processor.
The
•
Query Processor
actually executes the query.
•
The
Runtime Optimizer
modii es and adapts the query plans for
the Query Processor during the query evaluation.
It is obvious that under a streams environment, optimization tech-
niques are deployed before and after the execution of the query evalua-
tion. The situation is quite similar in terms of the data-intensive scientii c
workl ow. The optimization can be done both on the workl ow schema
level and on workl ow execution instance. In terms of specii c optimiza-
tion techniques, there are many categorization methods. Here we will
introduce two of them. Other methods can be referred to some other
references such as [39].
9.4.1.1
Static Optimization and Runtime Optimization
When a scientii c workl ow schema is given, we can evaluate and adjust
the execution plan, to make an optimized strategy to execute it. That is a
static optimization. During the execution of a workl ow, with the instance
data coming, the WFMS can know more and more extra information to
adapt its executing plan to gain a more optimized strategy. This is a
runtime optimization.
9.4.1.2
Semantic Optimization
Semantic optimization is a comparatively recent approach for the trans-
formation of given queries into equivalent alternative queries using
matching rules in order to select an optimal execution plan based on
the cost of executing alternatives [36,38]. To understand the semantic
optimization, we have to introduce two important concepts of semanti-
cally equivalent and semantic transformation.
Two plans are semantically equivalent if they return the same answer or
fuli ll the same task. A semantic transformation transforms a given
schema into a semantically equivalent one. Semantic optimization is the
process of determining the set of semantic transformations that results in
a semantically equivalent schema with a lower execution cost. The discus-
sion of optimization is indispensable in terms of any processing algorithm
and its corresponding execution plan.
XML stream-specii c optimization techniques have been well devel-
oped [36,38]. For example, the schema-based optimization (SQO) works
on one abstraction level, which uses schema knowledge to rewrite a query
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