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Titlebook: Provenance in Data Science; From Data Models to Leslie F. Sikos,Oshani W. Seneviratne,Deborah L. M Book 2021 Springer Nature Switzerland A

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書目名稱Provenance in Data Science
副標(biāo)題From Data Models to
編輯Leslie F. Sikos,Oshani W. Seneviratne,Deborah L. M
視頻videohttp://file.papertrans.cn/763/762055/762055.mp4
概述Presents a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations to be used for information processing, management, aggreg
叢書名稱Advanced Information and Knowledge Processing
圖書封面Titlebook: Provenance in Data Science; From Data Models to  Leslie F. Sikos,Oshani W. Seneviratne,Deborah L. M Book 2021 Springer Nature Switzerland A
描述RDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations.? This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself..? ? ? ? ? ? ?.Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attack mapsthat aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, c
出版日期Book 2021
關(guān)鍵詞Knowledge graph; Contextualized Knowledge graph; Data science; Data provenance; Provenance ontology; Scie
版次1
doihttps://doi.org/10.1007/978-3-030-67681-0
isbn_softcover978-3-030-67683-4
isbn_ebook978-3-030-67681-0Series ISSN 1610-3947 Series E-ISSN 2197-8441
issn_series 1610-3947
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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