M. Lacroix and P. Lavency. Preferences: Putting More Knowledge Into Queries. In Proceedings of the International Conference on Very Large Databases, pages 217--225, 1987.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....in practice. Some SCM systems select component versions through a set of configuration rules, using PROLOG like syntax as in SHAPE [30] or pattern matching rules as in CLEARCASE [31] The basic idea is that the first matching rule is applied. An alternate scheme is realized in preference clauses [29], where each configuration rule refines the results of the previous one, until an unambiguous version is selected. Such schemes cannot be expressed in feature logic directly, since a version S being unambiguous means that S =1 holds, and checking the cardinality depends on a specific ....

Lacroix, M., and Lavency, P. Preferences: Putting more knowledge into queries. In Proc. of the 13th International Conference on Very Large Data Bases (Brighton, 1987), P. M. Stocker and W. Kent, Eds., pp. 217--225.

....is a crisp constraint (a = 1 or 0) This is not equivalent when C i is a soft constraint since when C i is not completely satisfied, C j has a priority less than the one of C i . Let us now show how to represent nested requirements with preferences, such as the ones considered by database authors [30][31] by means of conditional prioritized constraints. Lacroix and Lavency [30] deal with requirements of the form C 1 should be satisfied, and among the solutions to C 1 (if any) the ones satisfying C 2 are preferred, and among those satisfying both C 1 and C 2 , those satisfying C 3 are ....

....constraint since when C i is not completely satisfied, C j has a priority less than the one of C i . Let us now show how to represent nested requirements with preferences, such as the ones considered by database authors [30] 31] by means of conditional prioritized constraints. Lacroix and Lavency [30] deal with requirements of the form C 1 should be satisfied, and among the solutions to C 1 (if any) the ones satisfying C 2 are preferred, and among those satisfying both C 1 and C 2 , those satisfying C 3 are preferred, and so on , where C 1 , C 2 , C 3 . are hard constraints. It should be ....

M. Lacroix and P. Lavency, "Preferences: Putting more knowledge into queries," in Proc. of the 13rd Inter. Conf. on Very Large Data Bases, Brighton, UK, 1987, pp. 217-225.

....max( P j (d) 1 P i (d) Using fuzzy sets in flexible querying: Why and how (D. Dubois H. Prade) 8 P i P j is a prioritized constraint with a variable priority. Let us now show how to represent nested requirements with preferences, such as the ones considered by database authors (Lacroix and Lavency, 1987; Bosc and Pivert, 1993) by means of conditional prioritized requirements. Lacroix and Lavency (1987) deal with requirements of the form P 1 should be satisfied, and among the solutions to P 1 (if any) the ones satisfying P 2 are preferred, and among those satisfying both P 1 and P 2 , ....

....Prade) 8 P i P j is a prioritized constraint with a variable priority. Let us now show how to represent nested requirements with preferences, such as the ones considered by database authors (Lacroix and Lavency, 1987; Bosc and Pivert, 1993) by means of conditional prioritized requirements. Lacroix and Lavency (1987) deal with requirements of the form P 1 should be satisfied, and among the solutions to P 1 (if any) the ones satisfying P 2 are preferred, and among those satisfying both P 1 and P 2 , those satisfying P 3 are preferred, and so on , where P 1 , P 2 , P 3 . are hard ....

Lacroix M., Lavency P. (1987) Preferences: Putting more knowledge into queries. Proc. of the 13rd Inter. Conf. on Very Large Data Bases, Brighton, UK, 217-225.

....query languages such as Datalog and SQL allow the user to specify only mandatory requirements on the data to be retrieved from a database. In many applications, it may be natural to express not only mandatory requirements but also preferences on the data to be retrieved. Lacroix and Lavency [12] extended SQL with a notion of preference and showed how the resulting query language could still be translated into the domain relational calculus. We explore the use of preference in databases in the setting of Datalog. We introduce the formalism of preference datalog programs (PDP) as ....

....purpose. PDPs extend datalog not only with constructs to specify which predicate is to be optimized and the criterion for optimization but also with constructs to specify which predicate to be relaxed and the criterion to be used for relaxation. We can show that all of the soft requirements in [12] can be directly encoded in PDP. We first develop a naively pruned bottom up evaluation procedure that is sound and complete for computing answers to normal and relaxation queries when the PDPs are stratified, we then show how the evaluation scheme can be extended to the case when the programs are ....

[Article contains additional citation context not shown here]

M. Lacroix and P. Lavency. Preferences: Putting More Knowledge into Queries. In Proc. 13th Intl. Conf. on Very Large Data Bases, pages 217--225, 1987.

....and a rule based approach in the same framework. Conditional prioritized requirements are a particular instance of rule based expressions which can be captured in the framework of a multiple criteria aggregation approach. This type of problem has been encountered in database querying systems by Lacroix and Lavency (1987) who deal with requirements of the form P 1 should be satisfied, and among the solutions to P 1 (if any) the ones satisfying P 2 are preferred, and among those satisfying both P 1 and P 2 , those satisfying P 3 are preferred, and so on , where P 1 , P 2 , P 3 . are binary constraints for ....

Lacroix, M.; Lavency, P. 1987. Preferences: Putting more knowledge into queries. Proc. of the 13th Inter. Conf. on Very Large Data Bases, Brighton, UK, pp. 217-225.

....in practice. Some SCM systems select component versions through a set of configuration rules, using PROLOG like syntax as in SHAPE [30] or pattern matching rules as in CLEARCASE [31] The basic idea is that the first matching rule is applied. An alternate scheme is realized in preference clauses [29], where each configuration rule refines the results of the previous one, until an unambiguous version is selected. Such schemes cannot be expressed in feature logic directly, since a version S being unambiguous means that S = 1 holds, and checking the cardinality depends on a specific ....

Lacroix, M., andLavency, P. Preferences: Putting more knowledge into queries. In Proc. of the 13th International Conference on Very Large Data Bases (Brighton, 1987), P. M. Stocker and W. Kent, Eds., pp. 217--225.

....only the mandatory requirements on the data to be retrieved from a database. In many applications, it is more natural to express queries in terms of both mandatory, or hard, requirements as well as preferences, or soft requirements. This idea has been explored in the past by Lacroix and Lavency [23], and extensions to traditional relational calculus have been proposed for this purpose. We have explored the concept of preference in the setting of datalog, a framework that offers more expressiveness than the relational calculus by its ability to support transitive closures and general ....

M. Lacroix and P. Lavency. Preferences: Putting More Knowledge into Queries. In Proc. 13th Intl. Conf. on Very Large Data Bases, pages 217--225, 1987.

....approaches to flexible querying have been proposed and can be divided into three groups : i) introduction of a complementary criterion in the query, ii) use of similarities and distances and iii) description of explicit preferences and weighting. Complementary Criterion In the PREFERENCES system (Lacroix and Lavency, 1987), a question is composed of a principal condition C and a complementary part P that is relative to the description of preferences, both of which are based on Boolean expressions. The meaning of this type of question is : find the tuples which satisfy C and rank them according to their ....

Lacroix M., Lavency P. (1987) Preferences : putting more knowledge into queries. Proc. of the 13th Very Large Data Bases Conference, Brighton, 217-225.

....of constraint satisfaction with errors. The related problem of approximate constraint satisfaction, where weights are assigned to individual combinations of values [34] 36] is motivated by machine vision as well. The expression of preferences in database queries is also a related problem [24]. Note that the concept of optimization can play a role in constraint satisfaction problems even when all constraints are satisfied; there may be an additional criterion to optimize among alternative solutions [5] 7] 37] Most of this paper will focus on methods for maximal constraint ....

M. Lacroix and P. Lavency. Preferences: putting more knowledge into queries. In Proceedings 15th International Conference on Very Large Data Bases, pages 217-- 225, 1987.

....query languages allow the user to express only the mandatory requirements on the data to be retrieved from a database. In many applications, it is more natural to express queries in terms of both mandatory, or hard, requirements as well as preferences, or soft requirements. Lacroix and Lavency [8] explore this idea with queries of the form select R where H prefer S whose result is the set of tuples from R that satisfy both H and S, if the set is nonempty; otherwise, i.e. if no tuple satisfies both H and S, the result is the set of tuples that satisfy just H. In other words, S is a ....

....predicate is to be optimized and the criterion for optimization as well as which predicate is to be relaxed and the criterion for relaxation. The contributions of this paper are two fold: 1. At the language level, we show that preference datalog can directly encode all of the soft requirements in [8]. We also show that the concept of preference provides a modular and declarative (i.e. logical) means for formulating optimization and relaxation queries in deductive databases. 2. At the computation level, we describe bottom up evaluation methods for preference datalog programs. The evaluation ....

[Article contains additional citation context not shown here]

M. Lacroix and P. Lavency. Preferences: Putting More Knowledge into Queries. In Proc. 13th Intl. Conf. on Very Large Data Bases, pages 217--225, 1987.

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M. Lacroix and P. Lavency. Preferences: Putting More Knowledge Into Queries. In Proceedings of the International Conference on Very Large Databases, pages 217--225, 1987.

No context found.

M. Lacroix, P. Lavency. Preferences: Putting More Knowledge into Queries. In Proc. of 13th Intl. Conf. on Very Large Data Bases, Roma, Italy, pp. 217--225, 1987.

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