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Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs (Advanced Information and Knowledge Processing) free ebook download

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Computer Science

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Author(s):	Leslie F. Sikos

Date:	2021	Format:	PDF EPUB	Language:	English	ISBN/ASIN:	3030676803
Pages:	1	OCR:		Quality:		ISBN13:
Uploader:	alex21s	Upload Date:	5/2/2021 8:39:52 AM

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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 maps that 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.

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	Title	Link	Password	Size (MB)	Thank to	Inform Admin
2998840	Book	https://nitroflare.com/view/582344A916A6...		0.00	alex21s

Tags: Advanced Context-Aware Data Graphs Information Knowledge Models Processing Provenance Science

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Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs (Advanced Information and Knowledge Processing) free ebook download

Category: Computer Science

Description: Click to see full description

Category:
Computer Science