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Decentralized Execution of Constraint Handling Rules for Ensembles
, 2013
"... CHR is a declarative, concurrent and committed choice rule-based constraint programming language. In this paper, we adapt CHR to provide a decentralized execution model for parallel and distributed programs. Specifically, we consider an execution model consisting of an ensemble of computing entities ..."
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Cited by 3 (3 self)
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CHR is a declarative, concurrent and committed choice rule-based constraint programming language. In this paper, we adapt CHR to provide a decentralized execution model for parallel and distributed programs. Specifically, we consider an execution model consisting of an ensemble of computing entities, each with its own constraint store and each capable of communicating with its neighbors. We extend CHR into CHR e, in which rewrite rules are executed at one location and are allowed to access the constraint store of its immediate neighbors. We give an operational semantics for CHR e, denoted ω e 0, that defines incremental and asynchronous decentralized rewriting for the class of CHR e rules characterized by purely local matching CHR semantics. We then give a safe encoding of the more general 1-neighbor restricted rules as 0-neighbor
Constraint Handling Rules with Multiset Comprehension Patterns
- In CHR’14
, 2014
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Modeling Datalog Fact Assertion and Retraction in Linear Logic
, 2006
"... Practical algorithms have been proposed to efficiently recompute the logical consequences of a Datalog program after a new fact has been asserted or retracted. This is essential in a dynamic setting where facts are frequently added and removed. Yet while assertion is logically well understood as inc ..."
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Practical algorithms have been proposed to efficiently recompute the logical consequences of a Datalog program after a new fact has been asserted or retracted. This is essential in a dynamic setting where facts are frequently added and removed. Yet while assertion is logically well understood as incremental inference, the mono-tonic nature of first-order logic is ill-suited to model retraction. As such, the traditional logical interpretation of Datalog offers at most an abstract specification of Datalog systems, but has tenuous rela-tions to the algorithms that perform efficient assertions and retrac-tions in practical implementations. This paper proposes a logical interpretation of Datalog based on linear logic. It not only captures the meaning of Datalog updates, but also provides an operational model that underlies the dynamic changes of the set of inferable facts, all within the confines of logic. We prove the correctness of this interpretation with respect to its traditional counterpart. 1.
Introduction Comprehensions in CHRcp Monotonicity Semantics Status Constraint Handling Rules with Multiset Comprehension Patterns
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Optimized Compilation of Multiset Rewriting with
, 2014
"... We extend the rule-based, multiset rewriting language CHR with multiset comprehension patterns. Multiset compre-hension provides the programmer with the ability to write multiset rewriting rules that can match a variable number of entities in the state. This enables implementing algorithms that coor ..."
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We extend the rule-based, multiset rewriting language CHR with multiset comprehension patterns. Multiset compre-hension provides the programmer with the ability to write multiset rewriting rules that can match a variable number of entities in the state. This enables implementing algorithms that coordinate large amounts of data or require aggregate operations in a declarative way, and results in code that is more concise and readable than with pure CHR. We call this extension CHRcp. In this paper, we formalize the operational semantics of CHRcp and define a low-level optimizing compilation scheme based on join ordering for the efficient execution of programs. We provide preliminary empirical results that demonstrate the scalability and effectiveness of this approach. ∗ This paper was made possible by grant NPRP 09-667-1-100, Effective Programming for Large Distributed Ensembles, from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of
