Toward sustainable development in constraint programming

Beldiceanu, Nicolas; Flener, Pierre; Monette, Jean-Noël; Pearson, Justin; Simonis, Helmut

doi:10.1007/s10601-013-9152-4

Cited by 3 publications

(1 citation statement)

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“…This is achieved by providing for each class of constraints, two of which are shown in this paper, a decoration table that maps the semantic letters of the output alphabet of the seed transducer to accumulator updates of the synthesised automaton-based constraints. This work contributes, in the context of time-series constraints, to the systematic reconstruction of the Global Constraint Catalogue that we have previously advocated [7]. These constraints are not only useful to describe properties of time series, but can be used in a wide range of applications to generate (parts of) new time series based on previously observed samples.…”

Section: Resultsmentioning

confidence: 99%

Using finite transducers for describing and synthesising structural time-series constraints

et al. 2015

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International audienceWe describe a large family of constraints for structural time series by means of function composition. These constraints are on aggregations of features of patterns that occur in a time series, such as the number of its peaks, or the range of its steepest ascent. The patterns and features are usually linked to physical properties of the time series generator, which are important to capture in a constraint model of the system, i.e. a conjunction of constraints that produces similar time series. We formalise the patterns using finite transducers, whose output alphabet corresponds to semantic values that precisely describe the steps for identifying the occurrences of a pattern. Based on that description, we automatically synthesise automata with accumulators, as well as constraint checkers. The description scheme not only unifies the structure of the existing 30 time-series constraints in the Global Constraint Catalogue, but also leads to over 600 new constraints, with more than 100,000 lines of synthesised code

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Section: Resultsmentioning

confidence: 99%