On the Effective Semantics of Nondeterministic, Nonmonotonic, Temporal Logic Databases

Giannotti, Fosca; Manco, Giuseppe; Nanni, Mirco; Pedreschi, Dino

doi:10.1007/10703163_5

Cited by 4 publications

(3 citation statements)

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“…Now, if M i is a stable model for P i * , then every stable model for M i ∪ P i+1 is a stable model for the program P * i+1 . Therefore, the stable models of stratified choice programs can be computed by modifying the iterated fixpoint procedure used for stratified programs so that choice models (rather than the least models) are computed for strata containing choice rules (Giannotti et al, 1998).…”

Section: Definition 22mentioning

confidence: 99%

“…Generalizing such notion, the LDL++ system supports the use of choice in programs that are XY-stratified with respect to negation. The following conditions are, however, enforced to assure the existence of stable models for a given program P (Giannotti et al, 1998):…”

Section: Choice and Aggregates In Xy-stratified Programsmentioning

confidence: 99%

“…After checking these conditions, the LDL++ compiler constructs syncbi(P ) by dropping the temporal variable from the choice goals and transforming the rest of the rules as described in the previous section. Then, the program syncbi(P ) so obtained is a stratified choice program and its stable models can be computed accordingly; therefore, each stable model for the original XY-stratified program P is computed by simply applying Procedure 2.1 with no modification (Zaniolo et al, 1997;Giannotti et al, 1998).…”

Section: Choice and Aggregates In Xy-stratified Programsmentioning

confidence: 99%

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The deductive database system [Lscr ][Dscr ][Lscr ]++

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This paper describes the [Lscr ][Dscr ][Lscr ]++ system and the research advances that have enabled its design and development. We begin by discussing the new nonmonotonic and nondeterministic constructs that extend the functionality of the [Lscr ][Dscr ][Lscr ]++ language, while preserving its model-theoretic and fixpoint semantics. Then, we describe the execution model and the open architecture designed to support these new constructs and to facilitate the integration with existing DBMSs and applications. Finally, we describe the lessons learned by using [Lscr ][Dscr ][Lscr ]++ on various tested applications, such as middleware and datamining.

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Section: Definition 22mentioning

confidence: 99%

Section: Choice and Aggregates In Xy-stratified Programsmentioning

confidence: 99%

Section: Choice and Aggregates In Xy-stratified Programsmentioning

confidence: 99%

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Key Constraints and Monotonic Aggregates in Deductive Databases

Zaniolo

2002

Computational Logic: Logic Programming and Beyond

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Abstract. We extend the fixpoint and model-theoretic semantics of logic programs to include unique key constraints in derived relations. This extension increases the expressive power of Datalog programs, while preserving their declarative semantics and efficient implementation. The greater expressive power yields a simple characterization for the notion of set aggregates, including the identification of aggregates that are monotonic with respect to set containment and can thus be used in recursive logic programs. These new constructs are critical in many applications, and produce simple logic-based formulations for complex algorithms that were previously believed to be beyond the realm of declarative logic.

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Nonmonotonic Reasoning in LDL++

Wang

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On the Effective Semantics of Nondeterministic, Nonmonotonic, Temporal Logic Databases

Cited by 4 publications

References 11 publications

The deductive database system [Lscr ][Dscr ][Lscr ]++

The deductive database system [Lscr ][Dscr ][Lscr ]++

Key Constraints and Monotonic Aggregates in Deductive Databases

Nonmonotonic Reasoning in LDL++

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