Provenance data science models (8 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2022
Serie: Advanced Information and Knowledge Processing, Buch 65 von 66. Buch 65 von 66 - Advanced Information and Knowledge Processing
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Sprache: Englisch
Verlag: Springer, 2021
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Weitere BilderSprache: Englisch
Verlag: Springer, 2022
Serie: Advanced Information and Knowledge Processing, Buch 65 von 66. Buch 65 von 66 - Advanced Information and Knowledge Processing
- Softcover
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Taschenbuch. Zustand: Neu. Provenance in Data Science | From Data Models to Context-Aware Knowledge Graphs | Leslie F. Sikos (u. a.) | Taschenbuch | Advanced Information and Knowledge Processing | xi | Englisch | 2022 | Springer | EAN 9783030676834 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 691…21 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Palgrave Macmillan, 2021
Serie: Advanced Information and Knowledge Processing, Buch 65 von 66. Buch 65 von 66 - Advanced Information and Knowledge Processing
- Hardcover
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Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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, knowl…edge 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, causing data reuse, trust, reproducibility and accountability issues.This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.
Sprache: Englisch
Verlag: Springer, 2021
Serie: Advanced Information and Knowledge Processing, Buch 65 von 66. Buch 65 von 66 - Advanced Information and Knowledge Processing
- Hardcover
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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-base…d 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, causing data reuse, trust, reproducibility and accountability issues.This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.
Sprache: Englisch
Verlag: Springer, 2022
Serie: Advanced Information and Knowledge Processing, Buch 65 von 66. Buch 65 von 66 - Advanced Information and Knowledge Processing
- Softcover
Anbieter: Buchpark, Trebbin, DeutschlandBuchpark
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Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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, knowl…edge 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, causing data reuse, trust, reproducibility and accountability issues.This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.
Sprache: Englisch
Verlag: Springer, 2022
Serie: Advanced Information and Knowledge Processing, Buch 65 von 66. Buch 65 von 66 - Advanced Information and Knowledge Processing
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 gra…ph-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, causing data reuse, trust, reproducibility and accountability issues.This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.
Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs
Sikos, Leslie F. (Edited by)/ Seneviratne, Oshani W. (Edited by)/ McGuinness, Deborah L. (Edited by)
Sprache: Englisch
Verlag: Springer, 2021
Serie: Advanced Information and Knowledge Processing, Buch 65 von 66. Buch 65 von 66 - Advanced Information and Knowledge Processing
- Hardcover
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Hardcover. Zustand: Brand New. 121 pages. 9.25x6.10x0.51 inches. In Stock.
