Discount Quality for Responsible Data Science: Human-in-the-Loop for Quality Data (SpringerBriefs in Applied Sciences and Technology) - Couverture souple

Couverture souple

ISBN 10 : 3032294800 ISBN 13 : 9783032294807

Editeur : Springer Nature Switzerland AG, 2026

Synopsis

This open access book presents a “discount”-quality approach to data preparation for scientific data analysis, a critical prerequisite in modern applications such as data analysis projects, Artificial Intelligence, and Machine Learning. It discusses advanced techniques for fostering responsible data science, i.e., designing sustainable data analysis pipelines based on Human-In-The-Loop (HITL) approaches to achieve high-quality data. It investigates developing task- and context-driven sustainable approaches for data preparation, drawing on methods and theories to reduce annotations and processing space and time, and considering the estimation of the necessary human computing effort and the requirements of the specific task. The contributions address key aspects related to data ecosystems, data preparation pipelines, data quality evaluation and improvement, on-demand approaches to data preparation, data enrichment, and human factors in data preparation.

A propos du livre

À propos de la quatrième de couverture

This open access book presents a "discount"-quality approach to data preparation for scientific data analysis, a critical prerequisite in modern applications such as data analysis projects, Artificial Intelligence, and Machine Learning. It discusses advanced techniques for fostering responsible data science, i.e., designing sustainable data analysis pipelines based on Human-In-The-Loop (HITL) approaches to achieve high-quality data. It investigates developing task- and context-driven sustainable approaches for data preparation, drawing on methods and theories to reduce annotations and processing space and time, and considering the estimation of the necessary human computing effort and the requirements of the specific task. The contributions address key aspects related to data ecosystems, data preparation pipelines, data quality evaluation and improvement, on-demand approaches to data preparation, data enrichment, and human factors in data preparation.

Détails de l'édition

• Éditeur: Springer Nature Switzerland AG
• Date d'édition: 2026
• Langue: anglais
• ISBN 10: 3032294800
• ISBN 13: 9783032294807
• Reliure: Tapa blanda
• Nombre de pages: 109
• Éditeur: Pernici Barbara, Catarci Tiziana, Palmonari Matteo, Simonini Giovanni
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3,69115 Heidelberg, Germany; Heidelberg; 69115; Allemagne