Data Independence
A major objective for the three-level architecture is to provide data independence, which
means that upper levels are unaffected by changes to lower levels. There are two kinds of
data independence: logical and physical.
Logical data Logical data independence refers to the immunity of the external
independence schemas to changes in the conceptual schema.
Changes to the conceptual schema, such as the addition or removal of new entities,
attributes, or relationships, should be possible without having to change existing external
schemas or having to rewrite application programs. Clearly, the users for whom the
changes have been made need to be aware of them, but what is important is that other users
should not be.
Physical data Physical data independence refers to the immunity of the conceptual
independence schema to changes in the internal schema.
Changes to the internal schema, such as using different file organizations or storage structures,
using different storage devices, modifying indexes, or hashing algorithms, should
be possible without having to change the conceptual or external schemas. From the users’
point of view, the only effect that may be noticed is a change in performance. In fact,
deterioration in performance is the most common reason for internal schema changes.
Figure 2.3 illustrates where each type of data independence occurs in relation to the threelevel
architecture.
The two-stage mapping in the ANSI-SPARC architecture may be inefficient, but provides
greater data independence. However, for more efficient mapping, the ANSI-SPARC
model allows the direct mapping of external schemas on to the internal schema, thus bypassing
the conceptual schema. This, of course, reduces data independence, so that every
time the internal schema changes, the external schema, and any dependent application
programs may also have to change.
A major objective for the three-level architecture is to provide data independence, which
means that upper levels are unaffected by changes to lower levels. There are two kinds of
data independence: logical and physical.
Logical data Logical data independence refers to the immunity of the external
independence schemas to changes in the conceptual schema.
Changes to the conceptual schema, such as the addition or removal of new entities,
attributes, or relationships, should be possible without having to change existing external
schemas or having to rewrite application programs. Clearly, the users for whom the
changes have been made need to be aware of them, but what is important is that other users
should not be.
Physical data Physical data independence refers to the immunity of the conceptual
independence schema to changes in the internal schema.
Changes to the internal schema, such as using different file organizations or storage structures,
using different storage devices, modifying indexes, or hashing algorithms, should
be possible without having to change the conceptual or external schemas. From the users’
point of view, the only effect that may be noticed is a change in performance. In fact,
deterioration in performance is the most common reason for internal schema changes.
Figure 2.3 illustrates where each type of data independence occurs in relation to the threelevel
architecture.
The two-stage mapping in the ANSI-SPARC architecture may be inefficient, but provides
greater data independence. However, for more efficient mapping, the ANSI-SPARC
model allows the direct mapping of external schemas on to the internal schema, thus bypassing
the conceptual schema. This, of course, reduces data independence, so that every
time the internal schema changes, the external schema, and any dependent application
programs may also have to change.
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