Qualità dei Dati : Concetti, Metodi e Tecniche = Data quality: Concepts, Methods and Techniques
Poor data quality can hinder or seriously damage the efficiency and effectiveness of organizations and businesses. The growing awareness of these repercussions has led to important public initiatives such as the promulgation of the "Data Quality Act" in the United States and the Directive 2003/98 of the European Parliament. The authors present a complete and systematic introduction to the wide range of problems related to data quality. The book starts with a detailed description of different dimensions of data quality, such as accuracy, completeness and consistency, and discusses its importance in relation to both different types of data, such as federated data, data present on the web and data with temporal dependencies, which to the different categories in which the data can be classified. The comprehensive description of techniques and methodologies from not only research in the area of data quality but also related areas, such as data mining, probability theory, statistical data analysis and machine learning, provides an excellent introduction to the state of the art. current art. The presentation is complemented by a short description and a critical comparison of practical tools and methodologies, which will help the reader to solve their quality problems.
كتب مشابهة
Scalable data management for future hardware
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New challenges in software engineering ; Vol 1
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Fundamentals of manufacturing engineering using digital visualization
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AI in drug discovery
Constitutes the refereed proceedings of the First international workshop on ai in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.
