Database Reference
In-Depth Information
Therefore, many approaches (e.g., (Gupta and
Mumick, 2005; Theodoratos and Sellis, 1997;
Mistry et al., 2001)) aim at optimizing mainte-
nance costs instead of query response. It is also
easy to incorporate both query evaluation costs
and materialized view maintenance costs in the
goal function (cf. Section 4.1).
such as part-of. Modern tuning tools shipped with
commercial database management systems rely on
query optimizers to explore implicit interrelation-
ships between candidate views (Zilio et al., 2004;
Dageville et al., 2004; Agrawal et al., 2004).
Static view Selection
Candidate Views
Most of the view selection techniques follow the
paradigm of
static view selection
(or
data ware-
house configuration
) (Theodoratos and Sellis,
1997), which selects views from a given input
candidate view set under storage and/or mainte-
nance constraints. The materialized views, once
determined offline, will not change over time.
Therefore, this line of work is good for cases where
the queries are relatively fixed or similar. When
the query patterns of the users change dramati-
cally, view selection has to be redone.
In the seminal work of Harinarayan et al.
(1996), both the query workload and the candidate
views are taken from the nodes in the data cube,
which are various group-bys for aggregation.
The interrelationship between candidates is then
represented by a lattice. For example, the database
in Example 1 gives rise to the lattice depicted in
Figure 2.
The edges in the lattice depict dependency
relationships. One view is dependent on another,
In general, there are prohibitively many views that
can be materialized. Even restricted to possible
combinations of selections and group-bys, the
number of candidate views is already exponen-
tial in terms of the size of the queries. Therefore,
the scope of candidate views has to be confined
to allow feasible tools. Most of the static view
selection techniques (cf. 4.3) restrict themselves
to a given set of candidate views. For example,
a natural scope is defined by all the aggregation
possibilities in the data cube (Gray et al., 1997),
which can be modeled as nodes in a lattice. Dy-
namic view selection approaches (cf. section 4.4)
make use of a windowed query history of users as
a candidate domain. Another type of candidates
are the views that are common subexpressions or
ancestors of queries. They are not optimal for a
single query, but they can benefit more than one
query and hence be a globally optimal choice.
Moreover, indexes can be also deemed as a special
type of views.
Figure 2. The lattice for the TPC-D database
Interrelationship Modeling
Candidate views have interactions. Selection of
one view may render materialization of another
view useless, while several views may have over-
lapping information and therefore duplications.
Dependencies between aggregations in a data
cube are modeled using a lattice. Analytic queries
involving selection and aggregation can be mod-
eled using hyperplanes covering. Chunks in view
selection approaches for MOLAP (Zhao et al.,
1997) usually have more intricate relationships
Search WWH ::
Custom Search