The Practitioner's Guide to Graph Data: Applying Graph Thinking and Graph Technologies to Solve Complex Problems (Paperback)
暫譯: 圖數據實務指南：應用圖思維與圖技術解決複雜問題

Gosnell, Denise, Broecheler, Matthias

The Practitioner's Guide to Graph Data: Applying Graph Thinking and Graph Technologies to Solve Complex Problems (Paperback)

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商品描述

Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you'll arrive at a unique intersection known as graph thinking.

Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases. You'll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application.

  • Build an example application architecture with relational and graph technologies
  • Use graph technology to build a Customer 360 application, the most popular graph data pattern today
  • Dive into hierarchical data and troubleshoot a new paradigm that comes from working with graph data
  • Find paths in graph data and learn why your trust in different paths motivates and informs your preferences
  • Use collaborative filtering to design a Netflix-inspired recommendation system

商品描述(中文翻譯)

圖形數據縮短了人類與計算機看待世界之間的差距。雖然計算機依賴靜態的數據行和列，但人類則通過關係來導航和推理生活。本實用指南展示了圖形數據如何將這兩種方法結合在一起。通過運用圖論、數據庫架構、分散式系統和數據分析的概念，您將抵達一個獨特的交匯點，稱為圖形思維。

作者Denise Koessler Gosnell和Matthias Broecheler向數據工程師、數據科學家和數據分析師展示如何使用圖形數據庫解決複雜問題。您將探索使用圖形技術構建的模板，以及示例，展示團隊如何在應用程序中思考圖形數據。

- 使用關聯技術和圖形技術構建示例應用程序架構
- 使用圖形技術構建Customer 360應用程序，這是當今最受歡迎的圖形數據模式
- 深入研究層次數據，並排除來自使用圖形數據的新範式的故障
- 在圖形數據中尋找路徑，了解為什麼您對不同路徑的信任會影響和告知您的偏好
- 使用協同過濾設計一個受Netflix啟發的推薦系統

作者簡介

Dr. Denise Gosnell's passion for examining, applying, and evangelizing the applications of graph data was ignited during her apprenticeship under Dr. Teresa Haynes and Dr. Debra Knisley during her first NSF Fellowship. This group's work was one of the earliest applications of neural networks and graph theoretic structure in predictive computational biology. Since then, Dr. Gosnell has built, published, patented, and spoke on dozens of topics related to graph theory, graph algorithms, graph databases, and applications of graph data across all industry verticals.

Currently, Dr. Gosnell is with DataStax where she aspires to build upon her experiences as a data scientist and graph architect. Prior to her role with DataStax, she built software solutions for and spoke at over a dozen conferences on permissioned blockchains, machine learning applications of graph analytics, and data science within the healthcare industry.

Dr. Matthias Broecheler is a technologist and entrepreneur with substantial research anddevelopment experience who is focused on disruptive software technologies and understanding complex systems. Dr. Broecheler's is known as an industry expert in graph databases, relational machine learning, and big data analysis in general. He is a practitioner of lean methodologies and experimentation to drive continuous improvement. Dr. Broecheler is the inventor of the Titan graph database and a founder of Aurelius.

作者簡介(中文翻譯)

德尼絲·戈斯內爾博士（Dr. Denise Gosnell）對於檢視、應用及推廣圖形數據應用的熱情，始於她在特蕾莎·海恩斯博士（Dr. Teresa Haynes）和黛布拉·克尼斯利博士（Dr. Debra Knisley）指導下的第一個國家科學基金會（NSF）獎學金實習。這個團隊的工作是神經網絡和圖論結構在預測計算生物學中的最早應用之一。自那時起，戈斯內爾博士已經建立、發表、申請專利並在數十個與圖論、圖算法、圖數據庫及圖數據在各行業應用相關的主題上發表演講。

目前，戈斯內爾博士在DataStax工作，她希望在數據科學家和圖形架構師的經驗基礎上進一步發展。在加入DataStax之前，她為許可區塊鏈、圖形分析的機器學習應用以及醫療行業中的數據科學構建軟體解決方案，並在十多個會議上發表演講。

馬提亞斯·布羅赫勒博士（Dr. Matthias Broecheler）是一位技術專家和企業家，擁有豐富的研究和開發經驗，專注於顛覆性軟體技術和理解複雜系統。布羅赫勒博士被認為是圖數據庫、關聯機器學習以及大數據分析領域的行業專家。他是精益方法論和實驗的實踐者，以推動持續改進。布羅赫勒博士是Titan圖數據庫的發明者，也是Aurelius的創始人。

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