Divesh Srivastava and Raghu Ramakrishnan. Pushing constraint selections. In Proceedings of the Eleventh ACM Symposium on Principles of Database Systems, San Diego, CA, June 1992.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....views that are not relevant to the query. However, the inverse rules can be computed once, and be applicable to any query. In order to remove irrelevant views from the rewriting produced by the inverse rules algorithm we need to apply a subsequent constraint propagation phase (as in [LFS97, SR92] A key advantage of the inverse rules algorithm is its conceptual simplicity and modularity. As shown in [DGL00] extending the algorithm to handle functional dependencies on the database schema, recursive queries or the existence of access pattern limitations can be done by adding another set ....

Divesh Srivastava and Raghu Ramakrishnan. Pushing constraint selections. In Proc. of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS), San Diego, CA., 1992.

....views that are not relevant to the query. However, the inverse rules can be computed once, and be applicable to any query. In order to remove irrelevant views from the rewriting produced by the inverse rules algorithm we need to apply a subsequent constraint propagation phase (as in [LFS97, SR92] The strength of the bucket al..gorithm is that it exploits the predicates in the query to prune significantly the number of candidate conjunctive rewritings that need to be considered. Checking whether a view should belong to a bucket can be done in time polynomial 22 in the size of the query ....

Divesh Srivastava and Raghu Ramakrishnan. Pushing constraint selections. In Proc. of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS), San Diego, CA., 1992.

....polynomial equality constraints, adopting local propagation steps for reasoning on constraints. A restricted form of linear constraints, called linear repeating points, was used to model infinite sequences of time points [KSW90, BNW91, NS92] More recent works on deductive databases [MFPR90, SR92, KS92, LS92] considered manipulation and repositioning of constraints for optimizing recursion. Algorithms for constraint algebra operators such as constraint joins, and generic global optimization were studied in [BJM93] and constraint approximation based optimization in [BW95] The work ....

D. Srivastava and R. Ramakrishnan. Pushing constraint selections. In Proc. 11th ACM SIGACTSIGMOD -SIGART Simposium on Principles of Database Systems, pages 301--315, 1992.

....queries is equivalent to D j= Q 1 Q 2 for both Datalog and its extension with constraints [38] However, this test is more complicated when D does not have INC. Other query optimization techniques, such as magic sets and selection pushing, apparently extend to constraints in a generic way [47,52], whether or not the constraint domain has INC. The use of (domain) constraints in integrity constraints 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, ....

D. Srivastava & R. Ramakrishnan, Pushing Constraint Selections, Journal of Logic Programming, 16, 361--414, 1993.

....hence not suitable for LP problems. A restricted form of linear constraints, called linear repeating points, was used to model infinite sequences of time points (Kabanza et al. 1990, Baudinet et al. 1991, Niezette and Stevenne, 1992) More recent work on deductive databases (Mumick et al. 1990, Srivastava and Ramakrishnan, 1992, Kemp and Stuckey, 1993, Kemp et al. 1989, Levy and Sagiv, 1992) concentrate on optimizing by repositioning constraints and assume the implementation of selection, projection and join and optimization of expressions involving these operators. Constraint algebra algorithms for specific ....

....each tuple. Some of these tuples might be redundant in the sense that omitting them does not alter the regular relation represented by the constraint relation. Clearly, a canonical form that eliminates such tuples would be desirable. However, the problem of detecting such tuples is co NP complete (Srivastava, 1992), and so we will perform only two simplifications of disjunctions: the deletion of each tuple with an inconsistent constraint, and the deletion of duplicates when all values are regular. Similarly, while it is theoretically possible to eliminate all existential quantifiers from our constraints ....

Srivastava, D. & Ramakrishnan, R. (1992). Pushing Constraint Selections. Proc. 11th PODS, 301--315, 1992.

....that are not relevant to the query. However, the inverse rules can be computed once, and be applicable to any query. In order to remove irrelevant views from the rewriting produced by the inverse rules algorithm we need to apply a subsequent constraint propagation phase (as in Levy et al. 1997; Srivastava and Ramakrishnan, 1992). The strength of the bucket al..gorithm is that it exploits the predicates in the query to prune signi cantly the number of candidate conjunctive rewritings that need to be considered. Checking whether a view should belong to a bucket can be done in time polynomial in the size of the query and ....

Srivastava, D. and Ramakrishnan, R. (1992). Pushing constraint selections. In Proc. of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS), San Diego, CA.

....and those which do have limited expressive power. The alternative is to take advantage of P and a specific query (or a class of queries) A transformation technique such as magic templates [206] produces a 24 program P mg that is equivalent to P for the specific query. Other techniques [146, 193, 237, 147] attempt to further limit execution by placing constraints at appropriate points in the program. Analyses can be used to check that execution of the resulting program terminates [151, 211, 35] although most work has ignored the capability of using constraints in the answers. Comparatively little ....

D. Srivastava & R. Ramakrishnan, Pushing Constraint Selections, Journal of 83 Logic Programming, 16, 361--414, 1993.

....the reachable space of Petri nets, viewed as special forms of automata with integers. The fixed point computation is optimized by integrating a method of path decomposition [20] This method can be regarded, from a logic programming point of view, as a method of redundant derivation elimination [47, 33, 25, 41], or, from a model checking point of view, as a method of partial order [30] a technique for combating the combinatorial explosion that results from interleaving concurrent independent transitions in all possible orders) 5.3 Real arithmetic An alternative proposed in [12] is to compute the ....

D. Srivastava and R. Ramakrishnan. "Pushing Constraints Selections". 11th ACM Symp. on Principles of Database Systems, San Diego, 1992, pp. 301--315.

....causality, persistence [DM87, KS86] recursive queries and so on. ffl Last, but not least, implementation issues must be addressed. Implementations of similar temporal reasoning systems [Dea89] implementations of constraint logic programming languages and constraint query languages [JMSY90] SR92] Sri92] and the algorithms of [Kou92] will be the starting points of this investigation. ....

Divesh Srivastava and Raghu Ramakrishnan. Pushing Constraint Selections. In Proceedings of the 11th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pages 301--315, 1992.

....This will be particularly challenging for the model of temporal tables due to the presence of indefinite information. Implementations of similar temporal reasoning systems [15] implementations of constraint logic programming languages [29] and implementations of constraint query languages [60, 59] will be the starting points of this investigation. Our models should also be extended with periodic data. Given the work of [31] this should not be a difficult task but any implementation will be substantially more complicated. Finally, the models of this paper must be enhanced with deductive ....

Divesh Srivastava and Raghu Ramakrishnan. Pushing Constraint Selections. In Proceedings of the 11th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pages 301--315, 1992.

....1 Given some definition of minimality of derivations. 6 CHAPTER 1. INTRODUCTION The framework is shown to be general in that it encompasses definitions discussed in the past. These include definitions given by Subramanian [Subramanian, 1989] and definitions given in analysis of databases [Srivastava and Ramakrishnan, 1992] and [Elkan, 1990] The framework provides important insights into the problem of detecting independence of queries from updates in databases, enabling us to develop new algorithms for solving the independence problem. 1.2.2 Automatically Detecting Irrelevance A major focus of the thesis is the ....

....be formulated as W I(OE; Delta; DI 4 ; D ) Couching Subramanian s definitions in our framework highlights some of the properties of her definitions, mainly the fact that removing irrelevant formulas may not always lead to speeding up inference. A definition of irrelevance is described in [Srivastava and Ramakrishnan, 1992]. Their definition is equivalent to strong irrelevance when DI 2 is quantified over the set of all derivations of the query, i.e. it is equivalent to SI(OE; Delta; DI 2 ; D ) Several resolution strategies are based on removing irrelevant clauses. For example, for refutation resolution, ....

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Srivastava, Divesh and Ramakrishnan, Raghu 1992. Pushing constraint selections. In Proceedings of the Eleventh ACM SIGACTSIGMOD -SIGART Symposium on Principles of Database Systems, San Diego, CA.

....of view, bottom up evaluation procedures for Datalog programs over integers constitute an interesting subject of research per se and have been extensively studied in recent years. One reason for this interest is the emergence of Constraint Logic Programming and Constraint Query Languages (e.g. [15, 16, 18, 19, 22, 23, 27, 29]) Another reason lies in the use of integers for representing temporal data and the use of bottom up evaluation for computing temporal queries [2, 3, 8] The rest of this report is organized as follows: In sections 2 we compare our results with related works, in section 3 and 4 we present some ....

....described here the method for the case where programs were made of at most three recursive rules. We believe that the underlying idea of the method is general and can be applied to the general case of n recursive rules. Moreover, using additional techniques such as constraint pushing (see, e.g. [29]) our method can be applied in many cases where the programs have more than one constraint per rule. We have assumed in this paper that the domain of the variables and constants were the domain of integer numbers, but our method can be applied if the domain of the variables and constants is the ....

D. Srivastava and R. Ramakrishnan. "Pushing Constraint Selections". Proc. 11th ACM Symp. on Principles of Database Systems, San Diego, 1992, pp. 301-315.

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Divesh Srivastava and Raghu Ramakrishnan. Pushing constraint selections. In Proceedings of the Eleventh ACM Symposium on Principles of Database Systems, San Diego, CA, June 1992.

No context found.

Srivastava, D. and Ramakrishnan, R. Pushing constraint selections. In Proceedings of the ACM Symposium on Principles of Database Systems, (1992) 301--315.

....to be strictly determinate. 5.2 Constraint propagation The introduction of conditions to promote early failure is similar to the refinement optimization of Marriot and Stuckey [8] and the use of constraint propagation is similar to the technique employed by Srivastava and R. Ramakrishnan [16]. However, the method of [8] deals only with bottomup information, corresponding to our success conditions, and not with context information. The technique of [16] uses a bi directional constraint propagation technique that incorporates top down information. However, both methods, which are ....

....Marriot and Stuckey [8] and the use of constraint propagation is similar to the technique employed by Srivastava and R. Ramakrishnan [16] However, the method of [8] deals only with bottomup information, corresponding to our success conditions, and not with context information. The technique of [16] uses a bi directional constraint propagation technique that incorporates top down information. However, both methods, which are designed for the CLP paradigm, deal only with constraints explicitly present in programs, whereas our method is more concerned with the inference of conditions from ....

D. Srivastava and R. Ramakrishnan. Pushing constraint selections. In ACM Symposium on Priciples of Database Systems, pages 301--315. ACM Press, 1992.

....approach is based on an item set construction similar to that used for LR parsers, and it is not clear whether this method can be adapted to propagate more general types of constraints. Finally, we note that a method involving constraint propagation was proposed by Srivastava and Ramakrishnan [SR92] for optimization of constraint query languages using bottom up evaluation. The constraints that are propagated are arithmetic constraints explicit in the program. However, the notion of abstraction that is crucial for termination is absent from their approach. 7 Conclusions and Future Directions ....

Divesh Srivastava and Raghu Ramakrishnan. Pushing constraint selections. In Eleventh Principles of Database Systems, pages 301--315, San Diego, CA, June 1992. ACM.

.... CLP: Necessary condition based techniques have been used in the context of CLP, to optimize operations over the constraint store [12, 15] Kemp and Stuckey [12] describe a technique to push constraint selections to achieve early failure by extending the earlier work of Ramakrishnan and Srivastava [24]. They also use techniques proposed by Mariott and Stuckey [15] to remove redundant operations due to the newly introduced constraints. The techniques employed in [12, 15] start with a source program, generate an intermediate program in which constraints are eagerly introduced and finally ....

D. Srivastava and R. Ramakrishnan. Pushing constraint selections. J. Logic Prog., 16:361--414, 1993.

.... CLP: Necessary conditionbased techniques have been used in the context of CLP, to optimize operations over the constraint store [12, 15] Kemp and Stuckey [12] describe a technique to push constraint selections to achieve early failure by extending the earlier work of Ramakrishnan and Srivastava [25]. They also use techniques proposed by Mariott and Stuckey [15] to remove redundant operations due to the newly introduced constraints. The techniques employed in [12, 15] start with a source program, generate an intermediate program in which constraints are eagerly introduced and finally ....

D. Srivastava and R. Ramakrishnan. Pushing constraint selections. J. Logic Prog., 16:361--414, 1993.

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D. Srivastava, R. Ramakrishnan. Pushing Constraint Selections. Proc. 11th ACM PODS, 301--316, 1992.

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Divesh Srivastava and Raghu Ramakrishnan. Pushing constraint selections. Journal of Logic Programming, 16(3--4):361--414, 1993.

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D. Srivastava and R. Ramakrishnan, "Pushing constraint selections," in Proc. of PODS, San Diego, CA., 1992, pp. 301--315.

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Divesh Srivastava and Raghu Ramakrishnan. Pushing constraint selections. In Proc. of PODS, pages 301-315, San Diego, CA., 1992.

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D. Srivastava and R. Ramakrishnan. Pushing constraint selections. In Proc. of PODS, pages 301--315, San Diego, CA., 1992. 12

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D. Srivastava, R. Ramakrishnan, Pushing Constraint Selections, Proc. 11th PODS, 301--315, 1992.

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Srivastava, D. and R. Ramakrishnan, "Pushing Constraints Selections". Proc. 11th ACM Symp. on Principles of Database Systems, San Diego, 1992, pp. 301-315.

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