Building the Unstructured Data Warehouse (Paperback)

Answers for many valuable business questions hide in text. How well can your existing reporting environment extract the necessary text from email, spreadsheets, and documents, and put it in a useful format for analytics and reporting? Transforming the traditional data warehouse into an efficient unstructured data warehouse requires additional skills from the analyst, architect, designer, and developer. This book will prepare you to successfully implement an unstructured data warehouse and, through clear explanations, examples, and case studies, you will learn new techniques and tips to successfully obtain and analyze text.

Master these ten objectives:

• Build an unstructured data warehouse using the 11-step approach
• Integrate text and describe it in terms of homogeneity, relevance, medium, volume, and structure
• Overcome challenges including blather, the Tower of Babel, and lack of natural relationships
• Avoid the Data Junkyard and combat the Spider's Web
• Reuse techniques perfected in the traditional data warehouse and Data Warehouse 2.0,including iterative development
• Apply essential techniques for textual Extract, Transform, and Load (ETL) such as phrase recognition, stop word filtering, and synonym replacement
• Design the Document Inventory system and link unstructured text to structured data
• Leverage indexes for efficient text analysis and taxonomies for useful external categorization
• Manage large volumes of data using advanced techniques such as backward pointers
• Evaluate technology choices suitable for unstructured data processing, such as data warehouse appliances

• Chapter 1 defines unstructured data and explains why text is the main focus of this book.
• Chapter 2 addresses the challenges one faces when managing unstructured data.
• Chapter 3 discusses the DW 2.0 architecture, which leads into the role of the unstructured data warehouse. The unstructured data warehouse is defined and benefits are given. There are several features of the conventional data warehouse that can be leveraged for the unstructured data warehouse, including ETL processing, textual integration, and iterative development.
• Chapter 4 focuses on the heart of the unstructured data warehouse: Textual Extract, Transform, and Load (ETL).
• Chapter 5 describes the 11 steps required to develop the unstructured data warehouse.
• Chapter 6 describes how to inventory documents for maximum analysis value, as well as link the unstructured text to structured data for even greater value.
• Chapter 7 goes through each of the different types of indexes necessary to make text analysis efficient. Indexes range from simple indexes, which are fast to create and are good if the analyst really knows what needs to be analyzed before the indexing process begins, to complex combined indexes, which can be made up of any and all of the other kinds of indexes.
• Chapter 8 explains taxonomies and how they can be used within the unstructured data warehouse.
• Chapter 9 explains ways of coping with large amounts of unstructured data. Techniques such as keeping the unstructured data at its source and using backward pointers are discussed. The chapter explains why iterative development is so important.
• Chapter 10 focuses on challenges and some technology choices that are suitable for unstructured data processing. In addition, the data warehouse appliance is discussed.
• Chapters 11, 12, and 13 put all of the previously discussed techniques and approaches in context through three case studies.

從數據倉庫專家Bill Inmon身上學習如何建立您業務現在所需的報告環境的基本技巧！

許多有價值的業務問題的答案都隱藏在文字中。您現有的報告環境能夠從電子郵件、試算表和文件中提取必要的文字並以有用的格式進行分析和報告嗎？將傳統數據倉庫轉變為高效的非結構化數據倉庫需要分析師、架構師、設計師和開發人員具備額外的技能。本書將使您能夠成功實施非結構化數據倉庫，並通過清晰的解釋、示例和案例研究，學習成功獲取和分析文本的新技術和技巧。

掌握以下十個目標：
- 使用11步方法構建非結構化數據倉庫
- 整合文本並以同質性、相關性、媒介、容量和結構來描述它
- 克服包含廢話、巴別塔和缺乏自然關係等挑戰
- 避免數據垃圾場並對抗蜘蛛網
- 重用在傳統數據倉庫和數據倉庫2.0中完善的技術，包括迭代開發
- 應用文本的提取、轉換和加載（ETL）的基本技術，如短語識別、停用詞過濾和同義詞替換
- 設計文檔庫存系統並將非結構化文本與結構化數據鏈接
- 利用索引進行高效的文本分析和分類法進行有用的外部分類
- 使用後向指針等高級技術管理大量數據
- 評估適用於非結構化數據處理的技術選擇，如數據倉庫設備

以下簡要描述了每個章節的內容：
- 第1章定義了非結構化數據並解釋了為什麼本書的主要焦點是文本。
- 第2章討論了管理非結構化數據時面臨的挑戰。
- 第3章介紹了DW 2.0架構，並介紹了非結構化數據倉庫的角色。定義了非結構化數據倉庫並給出了其好處。傳統數據倉庫的幾個特點可以用於非結構化數據倉庫，包括ETL處理、文本集成和迭代開發。
- 第4章重點介紹了非結構化數據倉庫的核心：文本的提取、轉換和加載（ETL）。
- 第5章描述了開發非結構化數據倉庫所需的11個步驟。
- 第6章描述了如何對文檔進行庫存以獲得最大的分析價值，以及如何將非結構化文本與結構化數據鏈接以獲得更大的價值。
- 第7章介紹了進行文本分析所需的各種不同類型的索引。索引的範圍從簡單索引（在索引過程開始之前，分析師真正知道需要分析的內容）到複雜的組合索引（可以由任何其他類型的索引組成）。
- 第8章解釋了分類法及其在非結構化數據倉庫中的應用。
- 第9章解釋了應對大量非結構化數據的方法。討論了保持非結構化數據在其源頭並使用後向指針的技術。該章還解釋了為什麼迭代開發如此重要。
- 第10章聚焦於非結構化數據處理的挑戰和一些適合的技術選擇。此外，還討論了數據倉庫設備。
- 第11、12和13章通過三個案例研究將所有先前討論的技術和方法放入上下文中。