Building the Data Warehouse

Author: W. H. Inmon

Publisher: John Wiley & Sons

ISBN: 0471774235

Category: Computers

Page: 576

View: 5527

The new edition of the classic bestseller that launched thedata warehousing industry covers new approaches and technologies,many of which have been pioneered by Inmon himself In addition to explaining the fundamentals of data warehousesystems, the book covers new topics such as methods for handlingunstructured data in a data warehouse and storing data acrossmultiple storage media Discusses the pros and cons of relational versusmultidimensional design and how to measure return on investment inplanning data warehouse projects Covers advanced topics, including data monitoring andtesting Although the book includes an extra 100 pages worth of valuablecontent, the price has actually been reduced from $65 to $55
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Building a Data Warehouse

With Examples in SQL Server

Author: Vincent Rainardi

Publisher: Apress

ISBN: 1430205288

Category: Computers

Page: 523

View: 5226

Here is the ideal field guide for data warehousing implementation. This book first teaches you how to build a data warehouse, including defining the architecture, understanding the methodology, gathering the requirements, designing the data models, and creating the databases. Coverage then explains how to populate the data warehouse and explores how to present data to users using reports and multidimensional databases and how to use the data in the data warehouse for business intelligence, customer relationship management, and other purposes. It also details testing and how to administer data warehouse operation.
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Building and Maintaining a Data Warehouse

Author: Fon Silvers

Publisher: CRC Press

ISBN: 9781420064636

Category: Computers

Page: 328

View: 4175

As it is with building a house, most of the work necessary to build a data warehouse is neither visible nor obvious when looking at the completed product. While it may be easy to plan for a data warehouse that incorporates all the right concepts, taking the steps needed to create a warehouse that is as functional and user-friendly as it is theoretically sound, is not especially easy. That’s the challenge that Building and Maintaininga Data Warehouse answers. Based on a foundation of industry-accepted principles, this work provides an easy-to-follow approach that is cohesive and holistic. By offering the perspective of a successful data warehouse, as well as that of a failed one, this workdetails those factors that must be accomplished and those that are best avoided. Organized to logically progress from more general to specific information, this valuable guide: Presents areas of a data warehouse individually and in sequence, showing how each piece becomes a working part of the whole Examines the concepts and principles that are at the foundation of every successful data warehouse Explains how to recognize and attend to problematic gaps in an established data warehouse Provides the big picture perspective that planners and executives require Those considering the planning and creation of a data warehouse, as well as those who’ve already built one will profit greatly from the insights garnered by the author during his years of creating and gathering information on state-of-the-art data warehouses that are accessible, convenient, and reliable.
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Building a Data Warehouse for Decision Support

Author: Vidette Poe,Patricia Klauer,Stephen Brobst

Publisher: Prentice Hall

ISBN: N.A

Category: Computers

Page: 285

View: 5824

Completely revised, expanded, and updated, this second edition gives extensive new coverage of data integration, management, indexing, cleansing, and transformation. The book covers powerful new multi-dimensional front-ends and conversion tools and gives detailed coverage of lifecycle issues.
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Building a Scalable Data Warehouse with Data Vault 2.0

Author: Dan Linstedt,Michael Olschimke

Publisher: Morgan Kaufmann

ISBN: 0128026480

Category: Computers

Page: 684

View: 7672

The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. "Building a Scalable Data Warehouse" covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. Important data warehouse technologies and practices. Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture. Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouse Demystifies data vault modeling with beginning, intermediate, and advanced techniques Discusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0
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Data Warehouse

From Architecture to Implementation

Author: Barry Devlin

Publisher: Addison-Wesley Professional

ISBN: 9780201964257

Category: Computers

Page: 432

View: 3909

Data warehousing is one of the hottest topics in the computing industry. Written by Barry Devlin, one of the world's leading experts on data warehousing, this book gives you the insights and experiences gained over 10 years and offers the most comprehensive, practical guide to designing, building, and implementing a successful data warehouse. Included in this vital information is an explanation of the optimal three-tiered architecture for the data warehouse, with a clear division between data and information. Information systems managers will appreciate the full description of the functions needed to implement such an architecture, including reconciling existing, diverse data and deriving consistent, valuable business information.
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Building the Unstructured Data Warehouse

Architecture, Analysis, and Design

Author: Bill Inmon,Krish Krishnan

Publisher: Technics Publications

ISBN: 1634620348

Category: Computers

Page: 216

View: 9060

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 The following outline briefly describes each chapter’s content: • Chapter 1 defines unstructured data and explains why text is the main focus of this book. The sources for text, including documents, email, and spreadsheets, are described in terms of factors such as homogeneity, relevance, and structure. • Chapter 2 addresses the challenges one faces when managing unstructured data. These challenges include volume, blather, the Tower of Babel, spelling, and lack of natural relationships. Learn how to avoid a data junkyard, which occurs when unstructured data is not properly integrated into the data warehouse. This chapter emphasizes the importance of storing integrated unstructured data in a relational structure. We are cautioned on both the commonality and dangers associated with text based on paper. • Chapter 3 begins with a timeline of applications, highlighting their evolution over the decades. Eventually, powerful yet siloed applications created a “spider’s web” environment. This chapter describes how data warehouses solved many problems, including the creation of corporate data, the ability to get out of the maintenance backlog conundrum, and greater data integrity and data accessibility. There were problems, however, with the data warehouse that were addressed in Data Warehouse 2.0 (DW 2.0), such as the inevitable data lifecycle. This chapter 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). This chapter has separate sections on extracting text, transforming text, and loading text. The chapter emphasizes the issues around source data. There are a wide variety of sources, and each of the sources has its own set of considerations. Extracting pointers are provided, such as reading documents only once and recognizing common and different file types. Transforming text requires addressing many considerations discussed in this chapter, including phrase recognition, stop word filtering, and synonym replacement. Loading text is the final step. There are important points to understand here, too, that are explained in this chapter, such as the importance of the thematic approach and knowing how to handle large volumes of data. Two ETL examples are provided, one on email and one on spreadsheets. • Chapter 5 describes the 11 steps required to develop the unstructured data warehouse. The methodology explained in this chapter is a combination of both traditional system development lifecycle and spiral approaches. • 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. The Document Inventory is discussed, which is similar to a library card catalog used for organizing corporate documents. This chapter explores ways of linking unstructured text to structured data. The emphasis is on taking unstructured data and reducing it into a form of data that is structured. Related concepts to linking, such as probabilistic linkages and dynamic linkages, are discussed. • 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. Both simple and complicated taxonomies are discussed. Techniques to help the reader leverage taxonomies, including using preferred taxonomies, external categorization, and cluster analysis are described. Real world problems are raised, including the possibilities of encountering hierarchies, multiple types, and recursion. The chapter ends with a discussion comparing a taxonomy with a data model. • 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. Ways of reducing the amount of data are presented, including screening and removing extraneous data, as well as parallelizing the workload. • Chapter 10 focuses on challenges and some technology choices that are suitable for unstructured data processing. The traditional data warehouse processing technology is reviewed. 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: the Ablatz Medical Group, the Eastern Hills Oil Company, and the Amber Oil Company.
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Implementing a Data Warehouse

A Methodology that Worked

Author: Bruce Russell Ullrey

Publisher: AuthorHouse

ISBN: 142599167X

Category: Computers

Page: 210

View: 8206

Illegal aliens and the staggering problems associated with every aspect of not controlling the illegal alien problem and the American borders. Taxes and the great problems enormous tax burdens cause for individuals, families, and business. Education: the good, the bad, and the ugly in America and its educational system. Teachers are getting ripped, the students are getting ripped, and so is America. Law and its negative effects on each one of us each and every day, no money no law for you. The end is the last chapter for America. If we do not take action in these areas, it will be the end for this country, as we know it today. Mr. and Mrs. Joe America must take action.
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Building a Better Data Warehouse

Author: Don Meyer,Casey Cannon

Publisher: Prentice Hall PTR

ISBN: N.A

Category: Computers

Page: 227

View: 2588

Just do it! Cut through the hype and get that data warehouse deployed! Based on Meyer and Cannon's extensive practical experience "Building a Better Data Warehouse" is a systematic guide to successful data warehouse deployment. It cuts through the hype, briefing managers on exactly what to expect building, piloting, deploying and maintaining a data warehouse. You'll learn how to take control of the process from start to finish-and discover the key success factors associated with data warehouses that deliver real business benefits. Understand the unique issues surrounding OLAP applications Compare data warehouses and data marts Plan your goals, architecture, infrastructure, platforms and tools Build the data model and the physical model Optimize performance, security and end-user access Maximize data integrity Meyer and Cannon pull no punches. They offer specific guidance for every critical decision, including hardware platforms, operating systems, databases tools and applications. They also provide comprehensive advice for both data modelers and DBAs, including proven techniques for completing data model deliverables and constructing the data warehouse. Building a Better Data Warehouse covers metadata, extraction programming, populating the data warehouse, end-user access tools, training, and much more. It's the one book every member of your data warehouse team should read.
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Essential Oracle8i Data Warehousing

Designing, Building, and Managing Oracle Data Warehouses

Author: Gary Dodge,Tim Gorman

Publisher: Wiley

ISBN: N.A

Category: Computers

Page: 928

View: 9327

"This book is the definitive guide for serious Oracle8i professionals and is required reading for all Oracle data warehousing practitioners."-Shannon Platz, Senior Director, Business Intelligence & Warehouse Global Service Line, Oracle Corporation A complete hands-on guide to Oracle8i and earlier versions In this updated and expanded edition of their critically acclaimed Oracle8 Data Warehousing, Gary Dodge and Tim Gorman clearly explain everything you'll need to know to build and manage a large, high-performance data warehouse using Oracle8i. They provide a technical roadmap to the specific Oracle8 or Oracle8i features that are relevant to designing, building, tuning, and administering an Oracle data warehouse. After a brief review of the basic concepts, you'll find descriptions of the various hardware platforms to support the Oracle data warehouse. The authors then cover the Oracle features that can enhance a large data warehouse, the design considerations for a warehouse, and the steps necessary to load data into the warehouse. You'll also find out how to perform parallel operations using Oracle8 and Oracle8i to accomplish massive tasks more quickly. And you'll discover the specific features and techniques for implementing a distributed architecture. With this book, you'll learn how to: - Design a data warehouse for optimum performance - Construct the data warehouse using Oracle8 and Oracle8i database technology - Load data into the data warehouse - Summarize and aggregate data within a warehouse - Administer and monitor a data warehouse for optimum performance - Build and manage very large (multiterabyte) data warehouses Visit our Web site at www.wiley.com/compbooks/ Visit the companion Web site at www.wiley.com/compbooks/dodge for scripts, extensions, and additional material.
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Enterprise Data Warehouse

Planning, Building, and Implementation

Author: Eric Sperley

Publisher: Prentice Hall

ISBN: 9780139058455

Category: Computers

Page: 333

View: 9403

Nuts-and-bolts data warehousing techniques that get the job done Finally, specifics This isnt a theory book: its an in-the-trenches, step-by-step guide to deploying data warehouses that align tightly with your business objectives. From Joint Application Development (JAD) techniques that maximize bang for the buck, to choosing the best hardware, software, and end-user access components, Eric Sperley delivers field-tested techniques you can rely on. Coverage includes: * Enterprise data modeling, including Transactional ER and Analytical Star approaches * Architecting the data warehouse: roles, tradeoffs, and compromises * Building metadata repositories that illuminate your data resources * Improving data quality - without breaking the bank * Sophisticated data mining: genetic algorithms, neural networks, clustering, decision trees, and beyond * Planning for scalability and easy updates Sperley delivers a practical, business-focused methodology thats flexible enough for any enterprise - and so detailed itll never leave you wondering what to do next. If your data warehouse must deliver sustainable competitive advantage, dont settle for anything less.
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Decision Support in the Data Warehouse

Author: Paul Gray,Hugh J. Watson

Publisher: Prentice Hall

ISBN: 9780137960798

Category: Computers

Page: 399

View: 7246

Most data warehousing books provide little information about the applications or tools that deliver the business value that the data warehouse provides. The title includes a comprehensive survey of tools and technologies available today. This book explores decision support in a data warehousing environment. Focus is on building front-end decision support systems.
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Building the Operational Data Store

Author: W. H. Inmon

Publisher: Wiley

ISBN: 9780471328889

Category: Computers

Page: 336

View: 6806

The most comprehensive guide to building, using, and managing the operational data store. Building the Operational Data Store, Second Edition. In the five years since the publication of the first edition of this book, the operational data store has grown from an intriguing concept to an exciting reality at enterprise organizations, worldwide. Still the only guide on the subject, this revised and expanded edition of Bill Inmon's classic goes beyond the theory of the first edition to provide detailed, practical guidance on designing, building, managing, and getting the most of an ODS. With the help of fascinating and instructive case studies, Inmon shares what he knows about: * How the ODS fits with the corporate information factory. * Different types of ODS and how to choose the right one for your organization. * Designing and building an ODS from scratch. * Managing and fine-tuning an ODS for peak efficiency. * ODS support technology. * The pros and cons of competing off-the-shelf ODS products. * The advantages and disadvantages of various hardware and software platforms. * Integrating the ODS with data marts. * Distributed metadata using the ODS. * Data aggregation within the ODS. * Business process reengineering and the ODS. * The role of standards in the ODS. Visit our Web site at www.wiley.com/compbooks/
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Data Warehousing

Using the Wal-Mart Model

Author: Paul Westerman

Publisher: Morgan Kaufmann

ISBN: 9781558606845

Category: Computers

Page: 297

View: 3445

Data Warehousing: Using the Wal-Mart Model is a practical guide to both the business and technical aspects of building a data warehouse for storing and accessing data in a strategically useful way. As its title suggests, it examines the development of the world's largest, most famous, and most ambitious commercial database. Westerman draws on this extensive, continuous example to teach readers the general principles and specific techniques needed to be a valuable part of their organisation's own data warehouse project, however large or small. What further sets it apart is its focus on the data.
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Data Warehousing

The Ultimate Guide to Building Corporate Business Intelligence

Author: SCN Education B.V.

Publisher: Springer Science & Business Media

ISBN: 3322849643

Category: Technology & Engineering

Page: 336

View: 1434

Rapid access to information is a prime requirement in any organization that wants to have a competitive edge in today's fast changing markets. How to retrieve information? How to capture data? How to format it? The answer lies in Data Warehousing. This HOTT Guide will give you access to all the essential information about the newest data storehouse: through articles by expert trendwachters on strategic considerations, how-to reports defining the various ways to extract the data needed for critical business decisions, technical papers clarifying technologies and tools, business cases and key concepts that will provide the reader with a comprehensive overview of a business solution that is already indispensable.
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Data Warehouse

Practical Advice from the Experts

Author: Joyce Bischoff,Ted Alexander

Publisher: N.A

ISBN: N.A

Category: Computers

Page: 428

View: 322

A practical handbook for the Data Warehouse that is designed to prepare people to progress toward providing any data anywhere, anytime.Data Warehouse: Practical Advice from the Experts will help technical managers, project managers, and members of data warehouse project teams in all aspects of planning, designing, developing, implementing, and administering a data warehouse. It is a practical book based on real-world experiences in building hundreds of data warehouses since each chapter is written by an internationally recognized authority in that particular field. An essential handbook for Technical Managers, Project Managers, Technical Personnel, Data Warehouse Project Teams, and end-users who want to provide access to the wealth of corporate data that has remained unavailable to those who need it.
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Data Warehousing

Building the Corporate Knowledge Base

Author: Tom Hammergren

Publisher: Coriolis Group

ISBN: N.A

Category: Data warehousing

Page: 470

View: 5601

This book covers the fundamentals of successfully designing, modeling and delivering a data warehouse and details techniques and links readers to a comprehensive methodology that enables system professionals to build and deliver a data warehouse that meets both corporate and management needs. The book features a skeleton project plan to assist readers in setting up their own project.
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Data warehouse performance

Author: William H. Inmon

Publisher: Wiley

ISBN: N.A

Category: Computers

Page: 444

View: 4197

Reduce operating and maintenance costs while substantially improving the performance of new and existing data warehouses and data marts Data Warehouse Performance This book tells you what you need to know to design, build, and manage data warehouses and data marts for optimum performance. Written by an all-star team of data warehouse pioneers and innovators-including Bill Inmon, "the father of the data warehouse," and Ken Rudin, one of the leading experts on performance-the book describes the layers of a high-performance data warehouse environment and guides the reader through their implementation and management. It also supplies proven techniques for supercharging the performance of existing environments. Crucial topics covered include: * Mitigating the impact of dormant data on performance * Data cleansing and implementation techniques * Implementing platform components like data marts to support scalability * Database design, sizing, and optimization techniques, including star schema and indexing * Hardware assessment, selection, and sizing * The role of monitors in balancing workload and assessing performance * Creating a service management contract to meet user expectations
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Building the Unstructured Data Warehouse

Architecture, Analysis, and Design

Author: W. H. Inmon,Krish Krishnan

Publisher: Technics Publications

ISBN: 1935504045

Category: Computers

Page: 216

View: 6291

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 The following outline briefly describes each chapter's content: • Chapter 1 defines unstructured data and explains why text is the main focus of this book. The sources for text, including documents, email, and spreadsheets, are described in terms of factors such as homogeneity, relevance, and structure.• Chapter 2 addresses the challenges one faces when managing unstructured data. These challenges include volume, blather, the Tower of Babel, spelling, and lack of natural relationships. Learn how to avoid a data junkyard, which occurs when unstructured data is not properly integrated into the data warehouse. This chapter emphasizes the importance of storing integrated unstructured data in a relational structure. We are cautioned on both the commonality and dangers associated with text based on paper.• Chapter 3 begins with a timeline of applications, highlighting their evolution over the decades. Eventually, powerful yet siloed applications created a “spider's web” environment. This chapter describes how data warehouses solved many problems, including the creation of corporate data, the ability to get out of the maintenance backlog conundrum, and greater data integrity and data accessibility. There were problems, however, with the data warehouse that were addressed in Data Warehouse 2.0 (DW 2.0), such as the inevitable data lifecycle. This chapter 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). This chapter has separate sections on extracting text, transforming text, and loading text. The chapter emphasizes the issues around source data. There are a wide variety of sources, and each of the sources has its own set of considerations. Extracting pointers are provided, such as reading documents only once and recognizing common and different file types. Transforming text requires addressing many considerations discussed in this chapter, including phrase recognition, stop word filtering, and synonym replacement. Loading text is the final step. There are important points to understand here, too, that are explained in this chapter, such as the importance of the thematic approach and knowing how to handle large volumes of data. Two ETL examples are provided, one on email and one on spreadsheets.• Chapter 5 describes the 11 steps required to develop the unstructured data warehouse. The methodology explained in this chapter is a combination of both traditional system development lifecycle and spiral approaches.• 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. The Document Inventory is discussed, which is similar to a library card catalog used for organizing corporate documents. This chapter explores ways of linking unstructured text to structured data. The emphasis is on taking unstructured data and reducing it into a form of data that is structured. Related concepts to linking, such as probabilistic linkages and dynamic linkages, are discussed.• 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. Both simple and complicated taxonomies are discussed. Techniques to help the reader leverage taxonomies, including using preferred taxonomies, external categorization, and cluster analysis are described. Real world problems are raised, including the possibilities of encountering hierarchies, multiple types, and recursion. The chapter ends with a discussion comparing a taxonomy with a data model.• 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. Ways of reducing the amount of data are presented, including screening and removing extraneous data, as well as parallelizing the workload.• Chapter 10 focuses on challenges and some technology choices that are suitable for unstructured data processing. The traditional data warehouse processing technology is reviewed. 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: the Ablatz Medical Group, the Eastern Hills Oil Company, and the Amber Oil Company.
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