Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs
暫譯: 數據科學中的來源：從數據模型到上下文感知知識圖譜

Sikos, Leslie F., Seneviratne, Oshani W., McGuinness, Deborah L.

Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs
  • 出版商: Springer
  • 出版日期: 2021-04-27
  • 售價: $6,360
  • 貴賓價: 9.5 折 $6,042
  • 語言: 英文
  • 頁數: 110
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3030676803
  • ISBN-13: 9783030676803
  • 相關分類: Data Science

    • 海外代購書籍(需單獨結帳)

商品描述

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 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.
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.

商品描述(中文翻譯)

基於RDF的知識圖譜需要額外的形式化方法才能完全具備上下文感知能力，本書將介紹這些內容。本書還提供了一系列的來源技術以及最先進的元數據增強、來源感知的知識圖譜表示，涵蓋多個應用領域，以展示如何結合基於圖的數據模型和來源表示。這對於使聲明具權威性、可驗證性和可重複性至關重要，例如在生物醫學、製藥和網絡安全應用中，數據來源和生成者可能與數據本身一樣重要。捕捉來源對於確保實驗結果的可靠性以及為患者和藥物安全、病理報告和醫療證據生成進行嚴謹設計的研究至關重要。同樣，來源對於網絡威脅情報儀表板和攻擊地圖也是必要的，這些儀表板和地圖聚合和/或融合來自不同數據來源的異構數據，以區分不重要的在線事件和危險的網絡攻擊，本書中也有相關的示範。若沒有來源，數據的可靠性和可信度可能會受到限制，導致數據重用、信任、可重複性和問責問題。

本書主要針對在其方法和途徑中利用知識圖譜的研究人員（這包括來自多個領域的研究人員，如網絡安全、電子健康、數據科學、語義網等）。本書收集了來源方法和技術的最新核心事實，並對現有方法進行了批判性評估。此外，還提供了結合數據科學和知識圖譜的新研究方向，這是一個日益重要的研究主題。

作者簡介

Dr. Leslie F. Sikos is a computer scientist specializing in artificial intelligence and data science, with a focus on cybersecurity applications. He holds two Ph.D. degrees and 20+ industry certificates. He is an active member of the research community as an author, editor, reviewer, conference organizer, and speaker, and a member of industry-leading organizations, such as the ACM and the IEEE. He contributed to international standards and developed state-of-the-art AI systems. Dr. Sikos published more than 20 books, including textbooks, monographs, and edited volumes.
Dr. Oshani W. Seneviratne is the Director of Health Data Research at the Institute for Data Exploration and Applications at the Rensselaer Polytechnic Institute (Rensselaer IDEA). She obtained her Ph.D. in Computer Science from Massachusetts Institute of Technology in 2014 under the supervision of Sir Tim Berners-Lee, the inventor of the World Wide Web. During her Ph.D., Oshani researched Accountable Systems for the Web. She invented a novel web protocol called HTTPA (HyperText Transfer Protocol with Accountability), and a novel provenance tracking mechanism called the Provenance Tracking Network. This work was demonstrated to be effective in several domains including electronic health care records transfer, and intellectual property protection in Web-based decentralized systems. At Rensselaer IDEA, Oshani leads the Smart Contracts Augmented with Analytics Learning and Semantics (SCALeS) project. The goal of this project is to predict, detect, and fix initially unforeseen situations in smart contracts utilizing novel combinations of machine learning, program analysis, and semantic technologies. Oshani is also involved in the Health Empowerment by Analytics, Learning, and Semantics (HEALS) Project. In HEALS she oversees the research operations targeted at the characterization and analysis of computational medical guidelines for chronic diseases such as diabetes, and the modeling of guideline provenance. Before Rensselaer, Oshani worked at Oracle specializing in distributed systems, provenance and healthcare-related research. She is the co-inventor of two enterprise provenance patents.
Prof. Deborah L. McGuinness is the Tetherless World Senior Constellation Chair and Professor of Computer, Cognitive, and Web Sciences at RPI. She is also the founding director of the Web Science Research Center and the CEO of McGuinness Associates Consulting. Deborah has been recognized with awards as a fellow of the American Association for the Advancement of Science (AAAS) for contributions to the Semantic Web, knowledge representation, and reasoning environments and as the recipient of the Robert Engelmore Award from the Association for the Advancement of Artificial Intelligence (AAAI) for leadership in Semantic Web research and in bridging Artificial Intelligence (AI) and eScience, significant contributions to deployed AI applications, and extensive service to the AI community. Deborah leads a number of large diverse data intensive resource efforts and her team is creating next-generation ontology-enabled research infrastructure for work in large interdisciplinary settings. Prior to joining RPI, Deborah was the acting director of the Knowledge Systems, Artificial Intelligence Laboratory and Senior Research Scientist in the Computer Science Department of Stanford University, and previous to that she was at AT\&T Bell Laboratories. Deborah consults with numerous large corporations as well as emerging startup companies wishing to plan, develop, deploy, and maintain semantic web and/or AI applications. Some areas of recent work include data science, next generation health advisors, ontology design and evolution environments, semantically enabled virtual observatories, semantic integration of scientific data, context-aware mobile applications, search, eCommerce, configuration, and supply chain management. Deborah holds a Bachelor of Math and Computer Science from Duke University, a Master of Computer Science from University of California at Berkeley, and a Ph.D. in Computer Science from Rutgers University.

作者簡介(中文翻譯)

德瑞斯·F·西科斯博士是一位專注於人工智慧和數據科學的計算機科學家，特別關注於網絡安全應用。他擁有兩個博士學位和20多個行業證書。他是研究社群的活躍成員，擔任作者、編輯、審稿人、會議組織者和演講者，並且是行業領先組織的成員，如ACM和IEEE。他對國際標準做出了貢獻，並開發了最先進的人工智慧系統。西科斯博士出版了超過20本書籍，包括教科書、專著和編輯卷。

奧莎尼·W·塞內維拉特尼博士是倫斯勒理工學院數據探索與應用研究所的健康數據研究主任。她於2014年在麻省理工學院獲得計算機科學博士學位，指導教授是萬維網的發明者蒂姆·伯納斯-李爵士。在攻讀博士學位期間，奧莎尼研究了網絡的可問責系統。她發明了一種名為HTTPA（具可問責性的超文本傳輸協議）的新型網絡協議，以及一種名為來源追蹤網絡的創新來源追蹤機制。這項工作在多個領域中被證明是有效的，包括電子健康記錄轉移和基於網絡的去中心化系統中的知識產權保護。在倫斯勒理工學院，奧莎尼領導著增強分析學習和語義的智能合約（SCALeS）項目。該項目的目標是利用機器學習、程序分析和語義技術的新型組合來預測、檢測和修復智能合約中最初未預見的情況。奧莎尼還參與了通過分析、學習和語義賦能的健康項目（HEALS）。在HEALS中，她負責針對慢性疾病（如糖尿病）的計算醫療指導的特徵化和分析研究操作，以及指導來源的建模。在加入倫斯勒之前，奧莎尼在甲骨文公司工作，專注於分佈式系統、來源和與健康相關的研究。她是兩項企業來源專利的共同發明人。

德博拉·L·麥金尼斯教授是無繩世界高級星座主席及倫斯勒理工學院計算機、認知和網絡科學教授。她也是網絡科學研究中心的創始主任和麥金尼斯顧問公司的首席執行官。德博拉因對語義網、知識表示和推理環境的貢獻而被美國科學促進會（AAAS）認可為會士，並因在語義網研究中的領導地位以及在人工智慧（AI）和電子科學之間的橋接、對已部署的AI應用的重大貢獻以及對AI社群的廣泛服務而獲得人工智慧促進協會（AAAI）的羅伯特·恩格爾莫獎。德博拉領導多個大型多樣化數據密集型資源項目，她的團隊正在為大型跨學科環境創建下一代本體啟用研究基礎設施。在加入倫斯勒之前，德博拉曾擔任斯坦福大學計算機科學系知識系統人工智慧實驗室的代理主任和高級研究科學家，之前她在AT&T貝爾實驗室工作。德博拉為許多大型企業以及希望規劃、開發、部署和維護語義網和/或AI應用的初創公司提供諮詢。最近的一些工作領域包括數據科學、下一代健康顧問、本體設計和演變環境、語義啟用的虛擬觀測站、科學數據的語義整合、上下文感知的移動應用、搜索、電子商務、配置和供應鏈管理。德博拉擁有杜克大學的數學和計算機科學學士學位，加州大學伯克利分校的計算機科學碩士學位，以及羅格斯大學的計算機科學博士學位。

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