Modélisation des données

Résumé

Dans la conception d'un système d'information, la modélisation des données est l'analyse et la conception de l'information contenue dans le système afin de représenter la structure de ces informations et de structurer le stockage et les traitements informatiques. Il s'agit essentiellement d'identifier les entités logiques et les dépendances logiques entre ces entités. La modélisation des données est une représentation abstraite, dans le sens où les valeurs des données individuelles observées sont ignorées au profit de la structure, des relations, des noms et des formats des données pertinentes, même si une liste de valeurs valides est souvent enregistrée. Merise Dans la méthodologie « Merise », le processus de développement du modèle de données implique d'analyser les types de données qui auront un sens dans le système d'information, et les relations entre différentes données de ce système. Ainsi le modélisateur doit s'exprimer avec des représentations des modèles de

Source officielle

https://fr.wikipedia.org/wiki/Mod%C3%A9lisation_des_donn%C3%A9es

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (92)

Publications associées

Chargement

Personnes associées (6)

Personnes associées

Chargement

Unités associées (2)

Unités associées

Chargement

Concepts associés (32)

Concepts associés

Chargement

Cours associés (33)

Cours associés

Chargement

Séances de cours associées (49)

Séances de cours associées

Chargement

Data-based model maintenance in the era of industry 4.0: A methodology

Antoine Pélissier

Despite the high number of investments for data-based models in the expansion of Industry 4.0, too little effort has been made to ensure the maintenance of those models. In a data-streaming environment, data-based models are subject to concept drifts. A concept drift is a change in data distribution which will, at some point, decrease the accuracy of the model. To address this problem, various frameworks are presented in the literature, but there is no optimal methodology for implementing them. This paper presents a methodology to implement a problem-oriented complete solution to ensure the maintenance of an industrial data-based model. The final drift-handling solution is composed of a sampling decision system and an update system. The methodology begins with a concept-drift identification phase. Solutions are then pre-selected based on the identified concept drifts. Next, an optimization problem is designed to select the solution that optimizes the costs and respects the constraints. To better link the concept drift characteristics and the drift-handling solutions, a causal concept-drift classification system is proposed. The industrial implementation of such a solution is discussed and several questions are raised. This paper presents an original and detailed methodology that shows encouraging results to address the model-maintenance challenge; however, concept drift identification, and links between concept-drift charac-teristics and drift detection, require further research.

ELSEVIER SCI LTD2022

Chargement

Data model description and translation using the meta-model some

EPFL1997

Chargement

We propose a novel machine learning (ML)-driven methodology to estimate biomechanical variables of interest traditionally obtained from upper-extremity musculoskeletal (MSK) modeling. MSK models facilitate personalized modeling, perform "what-if" analyses, and potentially enhance clinical decision-making. In certain settings, MSK models are driven by inertial motion capture (IMC) data. IMC systems are portable, user-friendly, and relatively affordable as well as provide additional biomechanical information. However, MSK models can be computationally expensive, often require extensive data, and can be prohibitively slow in making real-time predictions. Our ML method- involving a rigorous hyperparameters search-predicts kinematic and kinetic biomechanical information associated with human upper-extremity movements solely using IMC input data, thereby bypassing MSK models. The scaled cadaver-based MSK model was based on the Dutch Shoulder Model and the spine model implemented in the AnyBody Managed Model Repository. We employ neural networks (NNs), which are trained on IMC data obtained from five nondisabled subjects in subject- exposed (SE) settings (at least a trial from all subjects is used in training) and subject- naive (SN) settings (no information from test subjects is used in training). We compare the predictions of our ML model with that of an MSK model and find an excellent agreement in SE settings (average Pearson's r = 0.96, normalized RMSE (NRMSE) = 0.23) and strong correspondence in SN settings (average r = 0.89, NRMSE = 0.45). The linear model performed poorly for SN settings, which motivated a more complex ML model. Our findings suggest that an ML-based approach is highly viable for estimating upper-extremity biomechanical information solely from IMC data.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Chargement