Programmation procédurale

Résumé

En informatique, la programmation procédurale est un paradigme qui se fonde sur le concept d'appel procédural. Une procédure, aussi appelée routine, sous-routine ou fonction (à ne pas confondre avec les fonctions de la programmation fonctionnelle reposant sur des fonctions mathématiques), contient simplement une série d'étapes à réaliser. N'importe quelle procédure peut être appelée à n'importe quelle étape de l'exécution du programme, y compris à l'intérieur d'autres procédures, voire dans la procédure elle-même (récursivité). Avantages La programmation procédurale est un meilleur choix qu'une simple programmation séquentielle. Les avantages sont en effet les suivants :

la possibilité de réutiliser le même code à différents emplacements dans le programme sans avoir à le dupliquer (principe « DRY »), ce qui a pour effet la réduction de la taille du code source et un gain en localité des modifications, donc une amélioration de la maintenabilité (compréhension plus ra

Source officielle

https://fr.wikipedia.org/wiki/Programmation_proc%C3%A9durale

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A large part of software written today is reactive. In contrast to batch mode systems, a reactive program continuously adapts itself to new input which often requires a substantial amount of engineering. Traditionally, reactive programs are implemented in terms of callbacks. This approach leads to an inversion of control, which makes it hard to reason about control- and data flow. A number of programming models have been proposed to improve on callbacks, most prominently constraint imperative programming (CIP) and functional reactive programming (FRP). Both approaches have their individual advantages and limitations. CIP often handles multi-way data flow well but some event handling logic is difficult to express and data flow sometimes difficult to predict. In FRP, data flow is predictable but restricted to a single direction. Complex event logic can become complicated and the programmer is sometimes forced to escape the functional programming model imposed by FRP. Existing CIP and FRP implementations do not support incremental changes. Declarative programming with time-varying data structures therefore quickly becomes inefficient. In this thesis, we propose a reactive programming model that is functional reactive at its core but allows the programmer to escape the functional reactive paradigm when necessary. It improves on previous FRP systems and also on CIP in a number of ways. Our implementation, Scala.React, supports all host language expressions without special adaptation. We provide an imperative data flow language that simplifies the processing of complex event patterns. A user of Scala.React can safely use callbacks or a variant of callbacks that reduces inversion of control if a more declarative solution is not obvious. Our reactive sequence data structure propagates incremental changes. It supports both first-order reactivity by means of composition similar to functional collections via map, filter, fold and alike, as well as higher-order reactivity with support for time-varying collection elements. Reactive lenses provide a way to declaratively program with multi-way reactive data flow that allows for local reasoning and is therefore predictable. All of these abstractions are implemented on top of a general propagation system that guarantees data consistency, supports atomic updates, and provides isolation of different reactive parts of a program. The core system is extensible and abstracts from the details of individual abstractions.

EPFL2013

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Reasoning with large ontologies stored in relational databases: The OntoMinD approach

Christine Parent, Stefano Spaccapietra

A major obstacle to the development of ontologies in support of the Semantic Web is the poor capability of current ontology techniques to handle very large ontologies, in particular regarding scalability of reasoners. This paper builds on the assumption that very large ontologies can be efficiently handled using database management systems (DBMS), designed to provide best performance in storing, updating, and managing large volumes of data. To enhance DBMS with the reasoning functionality that characterizes ontology management, we propose to implement reasoning into the DBMS via a set of PL/SQL stored procedures. These procedures support all usual reasoning tasks: Class subsumption, property subsumption, class satisfiability. ABox consistency, and ABox realization. They perform these tasks at update time and materialize all inferred knowledge (facts and axioms) in the database. Contrarily to the inferencing at query time in most of existing works, our approach is designed to speed up ontology querying, which is supposed to represent the most frequent and therefore critical usage of ontologies. The paper discusses querying patterns and reports on benchmarking (with the LUBM benchmark) the performance of our prototype, called OntoMinD, compared to Oracle with Semantic Technologies. Benchmark results demonstrate the appropriateness of our approach. (C) 2010 Elsevier B.V. All rights reserved.

2010

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Structure-aware Synthesis for Predictive Woven Fabric Appearance

Wenzel Alban Jakob, Shuang Zhao

Woven fabrics have a wide range of appearance determined by their small-scale 3D structure. Accurately modeling this structural detail can produce highly realistic renderings of fabrics and is critical for predictive rendering of fabric appearance. But building these yarnlevel volumetric models is challenging. Procedural techniques are manually intensive, and fail to capture the naturally arising irregularities which contribute significantly to the overall appearance of cloth. Techniques that acquire the detailed 3D structure of real fabric samples are constrained only to model the scanned samples and cannot represent different fabric designs. This paper presents a new approach to creating volumetric models of woven cloth, which starts with user-specified fabric designs and produces models that correctly capture the yarn-level structural details of cloth. We create a small database of volumetric exemplars by scanning fabric samples with simple weave structures. To build an output model, our method synthesizes a new volume by copying data from the exemplars at each yarn crossing to match a weave pattern that specifies the desired output structure. Our results demonstrate that our approach generalizes well to complex designs and can produce highly realistic results at both large and small scales. © 2012 ACM 0730-0301/2012/08-ART75.

ACM2012

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