Maher, M. J. and Srivastava, D. (1996). Chasing Constrained Tuple-Generating Dependencies. In ACM Symposium on Principles of Database Systems, pages 128-138.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....being formally assessed. Recently, constrained dependencies have been introduced ( Mah94] BCW95] Mah97] They originate in the constraint programming eld and permit expression of semantic relations on variables, thus giving them an interpretation. The chase procedure has been redesigned in [MS96], still in a bottom up way, in order to deal with constrained tuple generating dependencies. This work in the dependency theory gives new perspectives for the top down chase procedure we present. The top down chase originates in the conceptual graphs model, which is a knowledge representation ....

....all these reasons, we plan to implement the chase of [BV84b] in order to practically compare their eciency. Dependencies might be divided into classes for which one or the other approach might be better. We must mention some optimisations to the bottom up approach have been made by magic sets in [MS96]. The principle of magic sets ( BMSU86] is to perform at compile time some optimizations that are usually performed at runtime, by rewriting the set of dependencies before inference. This leads to avoiding the generation of irrelevant facts during the process, which is the essence of the top down ....

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M. J. Maher and D. Srivastava. Chasing constrained tuple-generating dependencies. In PODS '96. Proceedings of the Fifteenth ACM SIGACTSIGMOD -SIGART Symposium on Principles of Database Systems, PODS

....constrained dependencies ( Mah94] BCW95] Mah97] and ordered dependencies [Ng99] have been introduced. They originate in the constraint programming eld and permit expression of semantic relations on variables, thus giving them an interpretation. The chase procedure has been redesigned in [MS96], still in a bottomup way, in order to deal with constrained tuple generating dependencies. This work in the dependency theory gives new perspectives for the top down chase procedure we present. The top down chase originates in the conceptual graphs model, which is a knowledge representation ....

....compare their eciency over a random dataset. We think dependencies might be divided into classes for which one or the other approach would be better but identifying them is still an open problem. We must mention that some optimisations to the bottom up approach have been made by magic sets in [MS96]. The principle of magic sets ( BMSU86] is to perform at compile time some optimizations that are usually performed at run time, by rewriting the set of dependencies before inference. This leads to avoiding the generation of irrelevant facts during the process, which is the essence of the topdown ....

[Article contains additional citation context not shown here]

M. J. Maher and D. Srivastava. Chasing constrained tuple-generating dependencies. In PODS '96. Proceedings of the Fifteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, PODS 1996, Montreal, Canada, June 3-5, 1996, volume 15, pages 127-138, New York, NY 10036, USA, 1996. ACM Press.

....on Temporal Databases Valid time: t v , transaction time t t . Many examples in [Jensen and Snodgrass, 1994] Complexity results for the implication problem [Baudinet et al. 1995] Not clear how to use CGDs for database design. Further generalizations: tuple generating constraint dependencies [Maher and Srivastava, 1996]. 111 2 Constraint Dependencies Functional dependencies are equality generating. In temporal databases it is natural to consider more general constraint generating dependencies [Baudinet et al. 1995] Example: The past never changes. 8x; x 0 ; t v ; t 0 v ; t t ; t 0 t : p(x; t v ; t t ....

Maher, M. J. and Srivastava, D. (1996). Chasing Constrained Tuple-Generating Dependencies. In ACM Symposium on Principles of Database Systems, pages 128--138.

....The data in such database systems is also subject to varied applicationspeci c semantic restrictions which, in traditional database systems, are represented as integrity constraints. One of the widest class of integrity constraints is the constrained tuple generating dependencies (CTGDs)[1], which generalize traditional dependencies such as FDs, MVDs, EGDs, TGDs[2] and CGDs[3] Here, we further extend this class to allow disjunctions. We call the resulting This work was partly supported by the Australian Research Council. class of integrity constraints disjunctive constrained ....

....applications where the potential for improvement is much greater than in traditional databases. Two key problems are testing query containment and testing integrity constraint implication. Previous researchers have developed and studied techniques for proving implication of integrity constraints [1, 3, 5, 6], and have proposed techniques for query optimization [4, 7] We extend this previous work to handle DCTGDs as follows. In Section 2, we de ne our generalized classes of integrity constraints and queries. In Section 3, we present a procedure for expanding a query Q in the presence of a set of ....

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M. J. Maher and D. Srivastava. Chasing constrained tuple-generating dependencies. In Proc. ACM Symp. PODS, New York, 1996.

....Queries in Extended Relational Databases Michael Maher and Junhu Wang CIT, Grith University, Brisbane, Australia 4111 M.Maher cit.gu.edu.au, jwang cit.gu.edu.au Abstract. We investigate the optimization of extended relational queries used in systems holding, for example, spatial, multimedia or constraint data. For such queries we must account for the built in relations speci c to the kind of data, and application dependent relationships between different relations. We show that the constraint database perspective and the use of ....

....unions are unnecessary. 1 Introduction Query optimization is highly successful for conventional relational databases. Current techniques are powerful and reliable enough to provide a reasonably ecient implementation of any query. Such optimization techniques can take into account common forms of application dependent semantic information expressed as data dependencies. However, this state of a airs does not hold for many modern extensions of relational databases, such as databases holding spatial or video data, and constraint databases. In such databases there are three issues that ....

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M. J. Maher and D. Srivastava. Chasing constrained tuple-generating dependencies. In Proc. ACM Symp. PODS, New York, 1996.

.... increases the flexibility of the integrity constraints, even when the data is purely relational (i.e. without constraints) Three classes of integrity constraints: functional dependencies, equality generating dependencies, and tuple generating dependencies have been extended with constraints [2,39,40]. The corresponding algorithms to test for implication of dependencies reduce to the usual algorithms when constraints are omitted. As above, the algorithms are also simplified when INC holds. 5.5 Non Monotonic Reasoning Although many default reasoning techniques (e.g. default logic [48] ....

M. Maher & D. Srivastava, Chasing Constrained Tuple-Generating Dependencies, Proc. ACM Symposium on Principles of Database Systems, 128--138, ACM Press, 1996.

....[19] but this is not necessary here since the dependencies we consider do not introduce existential quantification. Note, however, that a more general class of dependencies might need closure under existential quantification. For example, the constrained tuple generating dependencies of [29] need such closure. We briefly mention some useful constraint domains (for further introduction to these domains, see [19] the domains of linear arithmetic constraints over the integers, the rational number and the real numbers, are denoted by Z Lin , Q Lin and Lin respectively; the domain of ....

M. Maher & D. Srivastava, Chasing Constrained Tuple-Generating Dependencies, Proc. 15th ACM Symp. on Principles of Database Systems, 1996.

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Maher, M. J. and Srivastava, D. (1996). Chasing Constrained Tuple-Generating Dependencies. In ACM Symposium on Principles of Database Systems, pages 128-138.

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