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DynaMat
Pre-computation and materialization of views with aggregate functions
is a common technique in Data Warehouses. Due to the complex structure
of the warehouse and the different profiles of the users who submit
queries, there is need for tools that will automate the selection and
management of the materialized data. In this work we present DynaMat,
a system that dynamically materializes information at multiple levels
of granularity in order to match the workload but also takes into
account the maintenance restrictions for the warehouse, such as down
time to update the views and space availability.
DynaMat unifies the view selection and the view maintenance problems
under a single framework using a novel
The traditional interaction model for ad-hoc analysis in for the
system to process incoming queries independently and in many cases
sequentially. However, OLAP-style analysis many times gives rise to
simultaneous related queries. This is especially true in
report-generating applications where a pre-compiled set of aggregates
needs to be computed out of the raw data. Similarly many data-mining
applications have an a-priori knowledge of the queries that they want
to be executed. This presents a challenge for multi-query optimization
techniques to provide substantial performance gains by optimizing and
executing these queries as a unit. DynaMat's architecture is extended
to allow users to express multiple, possibly related, queries within a
single multi-query expression. We have incorporated optimization
techniques for the execution of such queries that exploit
inter-dependencies among them and better utilize the materialized
aggregates.
We believe that the main benefit of DynaMat, is that it represents a
complete self-tunable solution that relieves the warehouse
administrator from having to monitor and calibrate the system
constantly. In our experiments, we compare DynaMat against a system
that is given all queries in advance and the pre-computed optimal
static view selection. These experiments show that the dynamic view
selection outperforms the optimal static view selection and thus, any
sub-optimal static algorithm proposed in the literature. The
comparison is made, among others, on a new metric, the Detailed Cost
Savings Ratio introduced for quantifying the benefits of view
materialization against incoming queries.
DynaMat Papers
DynaMat People |
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