April 13, 2021

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DW 2.0: The Architecture for the Next Generation of Data Warehousing

DW 2.0: The Architecture for the Next Generation of Data Warehousing
Author : W.H. Inmon,Derek Strauss,Genia Neushloss
Publisher : Elsevier
Release Date : 2010-07-28
Category : Computers
Total pages :400
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DW 2.0: The Architecture for the Next Generation of Data Warehousing is the first book on the new generation of data warehouse architecture, DW 2.0, by the father of the data warehouse. The book describes the future of data warehousing that is technologically possible today, at both an architectural level and technology level. The perspective of the book is from the top down: looking at the overall architecture and then delving into the issues underlying the components. This allows people who are building or using a data warehouse to see what lies ahead and determine what new technology to buy, how to plan extensions to the data warehouse, what can be salvaged from the current system, and how to justify the expense at the most practical level. This book gives experienced data warehouse professionals everything they need in order to implement the new generation DW 2.0. It is designed for professionals in the IT organization, including data architects, DBAs, systems design and development professionals, as well as data warehouse and knowledge management professionals. * First book on the new generation of data warehouse architecture, DW 2.0. * Written by the "father of the data warehouse", Bill Inmon, a columnist and newsletter editor of The Bill Inmon Channel on the Business Intelligence Network. * Long overdue comprehensive coverage of the implementation of technology and tools that enable the new generation of the DW: metadata, temporal data, ETL, unstructured data, and data quality control.

Health Effects Assessment Summary Tables

Health Effects Assessment Summary Tables
Author : Anonim
Publisher : Unknown
Release Date : 1993
Category : Health risk assessment
Total pages :129
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Building the Unstructured Data Warehouse

Building the Unstructured Data Warehouse
Author : Bill Inmon,Krish Krishnan
Publisher : Technics Publications
Release Date : 2011-01-01
Category : Computers
Total pages :216
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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 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.

Nickel Hazards to Fish, Wildlife, and Invertebrates

Nickel Hazards to Fish, Wildlife, and Invertebrates
Author : Ronald Eisler
Publisher : Unknown
Release Date : 1998
Category : Nickel
Total pages :76
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Data Architecture: A Primer for the Data Scientist

Data Architecture: A Primer for the Data Scientist
Author : W.H. Inmon,Daniel Linstedt
Publisher : Morgan Kaufmann
Release Date : 2014-11-26
Category : Computers
Total pages :378
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Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it Shows how to turn textual information into a form that can be analyzed by standard tools Explains how Big Data fits within an existing systems environment Presents new opportunities that are afforded by the advent of Big Data Demystifies the murky waters of repetitive and non-repetitive data in Big Data

Data Warehouse 2.0

Data Warehouse 2.0
Author : Bill Inmon
Publisher : Unknown
Release Date : 2017
Category :
Total pages :129
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Over time the architecture of data warehouse has evolved towards an architecture known as Data Warehouse (DW) 2.0. In DW 2.0 there have been several advances including the inclusion of unstructured data into the data warehouse, the need for a formal and enterprise wide inclusion of corporate metadata. This course includes an overview to DW 2.0 including: An introduction to DW 2.0. We explore the traditional definition of a data warehouse as subject oriented, integrated, non-volatile, and time variant. We also explore the demands of unstructured data on the data warehouse and what makes the DW 2.0 architecture both unique and powerful. The DW 2.0 Lifecycle. Data can start off as interactive which is very current (up to the second), then integrated (current, hours to 5 years), then near line (less than current to over five years), and finally archival (older than five years). Archival within DW 2.0. We cover archival, which is when the primary usage of the data is done (that is, probability of access is low) yet the data still needs to be maintained by the organization. Data stored in archive can originate from the big data arena and contain both structured and unstructured data. Metadata is physically and tightly coupled with the data that resides in the archival sector. Data may be periodically retrieved from archival on a project basis for deeper analysis. DW 2.0 Components. We explore each component of the data warehouse architecture including applications, procedures, programs, databases, and transactions. The structures within DW 2.0 are organized by subject area such as Customer and Product. We will also discuss the Operational Data Store (ODS). DW 2.0 Database Design. The DW 2.0 contains different types of data. Therefore, there are different ways to do database design, which are covered within this video segment. We discuss the Interactive Sector, which demands a two to three second response time and 24 x 7 availability. The Integrated Sector of the architecture contains lots of data with this data being used for many different purposes. There is a heavy amount of indexing within the integrated sector. We also explore data mining within the integrated sector. With data mining the requirements are not provided or known, and usually the design resembles a spreadsheet in the form of flat records. We also discuss exploration processing and the role of historical data. DW 2.0 Integrated Design. We cover the integrated sector of DW 2.0 in d...

Data Warehouse 2.0

Data Warehouse 2.0
Author : William H. Inmon
Publisher : Unknown
Release Date : 2017
Category :
Total pages :129
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"Over time the architecture of data warehouse has evolved towards an architecture known as Data Warehouse (DW) 2.0. In DW 2.0 there have been several advances including the inclusion of unstructured data into the data warehouse, the need for a formal and enterprise wide inclusion of corporate metadata. An introduction to DW 2.0. We explore the traditional definition of a data warehouse as subject oriented, integrated, non-volatile, and time variant. We also explore the demands of unstructured data on the data warehouse and what makes the DW 2.0 architecture both unique and powerful. The DW 2.0 Lifecycle. Data can start off as interactive which is very current (up to the second), then integrated (current, hours to 5 years), then near line (less than current to over five years), and finally archival (older than five years). Archival within DW 2.0. We cover archival, which is when the primary usage of the data is done (that is, probability of access is low) yet the data still needs to be maintained by the organization. Data stored in archive can originate from the big data arena and contain both structured and unstructured data. Metadata is physically and tightly coupled with the data that resides in the archival sector. Data may be periodically retrieved from archival on a project basis for deeper analysis."--Resource description page.

Elements of the Theory of the Newtonian Potential Function

Elements of the Theory of the Newtonian Potential Function
Author : Benjamin Osgood Peirce
Publisher : Unknown
Release Date : 1888
Category : Electricity
Total pages :178
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OECD Economic Studies

OECD Economic Studies
Author : Anonim
Publisher : Unknown
Release Date : 1983
Category : Economic history
Total pages :129
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Economic Survey of Private Forestry Establishment Costs, England and Wales

Economic Survey of Private Forestry Establishment Costs, England and Wales
Author : Anonim
Publisher : Unknown
Release Date : 1964
Category : Forests and forestry
Total pages :129
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Australian Journal of Soil Research

Australian Journal of Soil Research
Author : Anonim
Publisher : Unknown
Release Date : 1993
Category : Soil research
Total pages :129
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The Quarterly Journal of Pure and Applied Mathematics

The Quarterly Journal of Pure and Applied Mathematics
Author : Anonim
Publisher : Unknown
Release Date : 1881
Category : Mathematics
Total pages :129
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Experimental Investigation of Transient Aerodynamics in Vehicle Interactions

Experimental Investigation of Transient Aerodynamics in Vehicle Interactions
Author : Amy Langhua Chen
Publisher : Unknown
Release Date : 1997
Category :
Total pages :228
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Bulletin

Bulletin
Author : Portland Cement Association. Research and Development Laboratories
Publisher : Unknown
Release Date : 1961
Category : Portland cement
Total pages :129
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Journal of Business & Economic Statistics

Journal of Business & Economic Statistics
Author : American statistical association
Publisher : Unknown
Release Date : 2021
Category :
Total pages :129
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