View Management Techniques and Their Application to Data Stream Management - Evolving Application Domains of Data Warehousing and Mining - page 90

Figure 1. Classification of view selection techniques

A taxonomy of view
Selection Approaches

Resource Constraints

Due to the vast volume of data in OLAP systems,
storage space is usually the first issue to be consid-
ered. However, regarding materialized views, the
limit on maintenance time also becomes vital. Too
much materialization can make the maintenance
time prohibitively long and hence decrease the
availability of the OLAP systems.

The taxonomy which we used for classifying view
selection techniques is illustrated in Figure 1.

Configuration Evolution

One most characteristic dimension of view se-
lection techniques is whether the selected views
evolve over time. Most of the techniques are of-
fline/static and will not change once the selection
is done. In contrast, online/dynamic approaches,
such as WATCHMAN (Scheuermann et al., 1996)
and DynaMat (Kotidis and Roussopoulos, 1999),
adjust the view selection following similar ideas
as semantic caching (cf. Section 4.4). Inside the
scope of dynamic view selection, there are two
significant issues: namely the caching policy and
the reuse policy. The caching policy is the way
how the view cache admits/evicts views, while
the reuse policy describes how the cached views
are used to speed up later queries.

Optimization Goals

Ideally, view selection techniques are aimed at
query response time. However, this goal is usu-
ally too expensive to achieve. Many existing
approaches opt for some other relevant measures
such as the benefit of a unit storage space. It has
been shown that materializing additional views
may reduce the overall maintenance costs in
presence of a set of pre-determined materialized
views (Ross et al., 1996; Labio et al., 1997). With
the costs of extra storage decreasing dramatically,
the update window, however, keeps shrinking.