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

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)

圖形數據彌補了人類和計算機對世界的觀點之間的差距。電腦依賴於靜態的數據行和列，而人們通過關係來導航和推理生活。這本實用指南演示了圖形數據如何將這兩種方法結合在一起。通過使用圖論、數據庫模式、分佈式系統和數據分析的概念，您將達到一個獨特的交集，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了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應用程序的模板，並通過示例了解團隊如何思考應用程序中的圖形數據。

- 使用關聯和圖形技術構建示例應用程序架構
- 使用圖形技術構建客戶360應用程序，這是當今最流行的圖形數據模式
- 深入研究分層數據並解決與圖形數據一起工作帶來的新範式問題
- 在圖形數據中查找路徑，並了解為什麼對不同路徑的信任會影響和指導您的偏好
- 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)

Dr. Denise Gosnell（丹尼絲·高斯內爾博士）對於研究、應用和推廣圖形數據的應用充滿熱情，這一熱情在她首次獲得NSF獎學金期間，在Teresa Haynes博士和Debra Knisley博士的指導下被點燃。這個團隊的工作是神經網絡和圖論結構在預測性計算生物學中最早的應用之一。此後，Gosnell博士在圖論、圖算法、圖數據庫以及圖形數據在各個行業垂直領域的應用方面建立、發表、申請專利並發表演講。

目前，Gosnell博士在DataStax任職，她希望在作為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，她為許多會議建立軟件解決方案並發表演講，內容涉及許可區塊鏈、圖形分析的機器學習應用以及醫療保健行業中的數據科學。

Dr. Matthias Broecheler（馬蒂亞斯·布羅赫勒博士）是一位技術專家和企業家，具有豐富的研究和開發經驗，專注於破壞性軟件技術和對複雜系統的理解。布羅赫勒博士在圖形數據庫、關聯機器學習和大數據分析方面被譽為行業專家。他是精益方法和實驗的實踐者，以推動持續改進。布羅赫勒博士是Titan圖形數據庫的發明人，也是Aurelius的創始人之一。

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