View Management Techniques and Their Application to Data Stream Management - Evolving Application Domains of Data Warehousing and Mining - page 93

is to generate multiple view processing plans
(MVPPs), which are query processing plans for
multiple queries using intermediate views. With
an emphasis on shared join computation, they map
the MVPP generation problem into a 0-1 integer
programming problem. In the second step, given
an optimal MVPP, cost driven optimization with
heuristics is performed to select views under
storage constraints.
Because of the high complexity in the tech-
niques above, heuristics pruning the search space
are necessary. Baralis et al. (1997) explore some
heuristics to reduce the search space and promote
reuse of views. A view is worth considering if it
corresponds to a user query or it is the least com-
mon ancestor (in the lattice) of two worthy views.
They also observe that it is often advantageous
to group by key attributes instead of non-key at-
tributes in terms of reuse.
Mistry et al. (2001) propose a framework
tightly integrated with a query optimizer to select
materialized views with the goal of reducing
overall maintenance costs. The query optimizer is
used to select an optimal maintenance plan out of
a search space consisting of recomputation plans
and incremental plans for materialized views.
Given an optimal maintenance plan, a cost driven
greedy algorithm is employed to select appropriate
additional views.
The lattice based techniques assume that the
candidate views are given as input, i.e., nodes in
the lattice. Approaches based on multiple query
optimization explore a broader scope, including
the original queries in the workload and their com-
mon subexpressions. Therefore, sub-optimality
may result from their restricts over the candidate
views.
AutoAdmin ((Chaudhuri and Narasayya,
2007), http://research.microsoft.com/DMX/
autoadmin/) is an industrial strength view
selection tool emphasizing practical SQL que-
ries and scalability for commercial settings. The
project started dealing with automatic index
selection (Chaudhuri and Narasayya, 1997), and

was extended (Agrawal et al., 2000) to allow for
selection of materialized views.
The approach has several advantages. First,
it is tightly integrated with the query optimizer,
and hence the selected structures are surely to be
made use of during query evaluation. Second,
neither a restricted set of candidate views has to
be provided in advance nor the interrelationships
between candidate views have to be modeled
explicitly. Third, a query language as rich as SQL
is considered with effective heuristics to ensure
(near-)optimality.

dynamic view Selection

Static view selection techniques, though very
effective, still suffer from a variety of problems.
First, they rely on a pre-compiled query workload,
and may not perform well for ad hoc queries.
Second, the resource constraints such as space and
maintenance time may change over time, while
the materialized views are fixed once selected.
Third, static view selection is usually unable to
meet both the space bound and the maintenance
bound at the same time. For example, if space is
sufficiently large, we can materialize transient
views, which can be dropped before the update
window. Finally, in order to adapt to the evolution
in reality, administrating efforts such as monitor-
ing and reconfiguration cannot be avoided. Based
on the above observations, another paradigm of
view selection techniques, called dynamic view
selection or view caching , is proposed to remedy
such problems.
Caching can be roughly divided into two
categories: physical caching and semantic cach-
ing . Physical caching refers to the mechanism
employed in operating systems and traditional
relational databases, where some physical stor-
age unit such as a page or a tuple is kept in cache.
Semantic caching takes advantage of high level
knowledge about the data being cached, and keeps
track of the semantic description of the cached
data (Dar et al., 1996). In particular, caching of