Databases Reference
In-Depth Information
Preventing Condition Activation
Given a transaction, the problem is to find an additional set of insertions
and/or deletions of base facts to be appended to the original transaction such
that it is guaranteed that no changes in the specified condition will occur.
More details about this framework can be found in [62]. Problems
related to DB schema validation, like satisfiability checking, view liveliness,
or redundancy of integrity constraints have been investigated in depth in
[67]. Recently, [68] showed that query containment can also be reformu-
lated as a view updating problem and, thus, can be also specified by means of
the downward interpretation.
4.5
Deductive Database System Prototypes
Results from the large amount of theoretical research devoted to deductive
DBs have both penetrated current relational DBMSs and inspired several
extensions to the relational model. Furthermore, this research has material-
ized in some prototypes of deductive DBMSs [13, 69]. Among these devel-
oped systems are Aditi, CORAL, DECLARE, Glue-Nail (see [70] for
descriptions and references), LDL [71], EKS-V1 [72, 73], XSB [74], Validity
[75], FOLRE [76], and the two prototypes developed during the IDEA
Project [77].
Table 4.2 summarizes some aspects of those systems. We have consid-
ered only the aspects directly related to the main topics addressed in this
chapter, that is, deductive DB definition, query processing, and update proc-
essing. Table 4.2 is both an adaptation and an extension of [69], which pro-
vides a wider comparison for some of the considered aspects. Relevant issues
considered for each aspect are the following.
Deductive database definition. Deductive rules and integrity con-
straints are the key concepts of deductive DBs. All the systems allow
the definition of recursive rules that may contain negative literals in
the body, while only some of them allow the definition of integrity
constraints.
·
Query processing. Not all the systems provide the three basic
approaches to query evaluation. We distinguish whether a system
provides a top-down (TD), bottom-up (BU), or magic sets (MS)
approach. Most of the systems incorporate additional optimizations
during query evaluation, in addition to the general approaches con-
sidered in this chapter (see [69]).
·
