Data-Centric Safety
Author | : Alastair Faulkner |
Publisher | : Elsevier |
Total Pages | : 542 |
Release | : 2020-05-27 |
ISBN-10 | : 9780128233221 |
ISBN-13 | : 0128233222 |
Rating | : 4/5 (222 Downloads) |
Download or read book Data-Centric Safety written by Alastair Faulkner and published by Elsevier. This book was released on 2020-05-27 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-Centric Safety presents core concepts and principles of system safety management, and then guides the reader through the application of these techniques and measures to Data-Centric Systems (DCS). The authors have compiled their decades of experience in industry and academia to provide guidance on the management of safety risk. Data Safety has become increasingly important as many solutions depend on data for their correct and safe operation and assurance. The book's content covers the definition and use of data. It recognises that data is frequently used as the basis of operational decisions and that DCS are often used to reduce user oversight. This data is often invisible, hidden. DCS analysis is based on a Data Safety Model (DSM). The DSM provides the basis for a toolkit leading to improvement recommendations. It also discusses operation and oversight of DCS and the organisations that use them. The content covers incident management, providing an outline for incident response. Incident investigation is explored to address evidence collection and management.Current standards do not adequately address how to manage data (and the errors it may contain) and this leads to incidents, possibly loss of life. The DSM toolset is based on Interface Agreements to create soft boundaries to help engineers facilitate proportionate analysis, rationalisation and management of data safety. Data-Centric Safety is ideal for engineers who are working in the field of data safety management.This book will help developers and safety engineers to: - Determine what data can be used in safety systems, and what it can be used for - Verify that the data being used is appropriate and has the right characteristics, illustrated through a set of application areas - Engineer their systems to ensure they are robust to data errors and failures