Building the Unstructured Data Warehouse
Architecture, Analysis, and Design
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Learn essential techniques from data warehouse legend Bill Inmon on how to build the reporting environment your business needs now! 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 unstru...
Learn essential techniques from data warehouse legend Bill Inmon on how to build the reporting environment your business needs now! 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
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