November 27, 2020

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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.

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...

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.

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.

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

Building a Scalable Data Warehouse with Data Vault 2.0

Building a Scalable Data Warehouse with Data Vault 2.0
Author : Dan Linstedt,Michael Olschimke
Publisher : Morgan Kaufmann
Release Date : 2015-09-15
Category : Computers
Total pages :684
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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

Cell Encapsulation Technology and Therapeutics

Cell Encapsulation Technology and Therapeutics
Author : Willem_M. Kühtreiber,Robert P. Lanza,William L. Chick
Publisher : Springer Science & Business Media
Release Date : 2013-12-01
Category : Medical
Total pages :450
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The concept of using encapsulation for the immunoprotection of transplanted cells was introduced for the first time in the 1960s. "[Microencapsulated cells] might be protected from destruction and from partici pation in immunological processes, while the enclosing membrane would be permeable to small molecules of specific cellular product which could then enter the general extracellular compartment of the recipient. For instance, encapsulated endocrine cells might survive and maintain an effective supply of hormone." (Chang, Ph. D. Thesis, McGill University, 1965; Chang et aI., Can J Physiol PharmacoI44:115-128, 1966). We asked Connaught Laboratories, Ltd., in Toronto to put this concept into practice. In 1980, Lim and Sun from Connaught Laboratories reported on the successful implantation of poly-I-Iysine-alginate encapsu lated rat islets into a foreign host. [Lim and Sun, Science 210:908-909, 1980]. Now many groups around the world are making tremendous progress in the encapsulation of a multitude of cell types. Kiihtreiber, Lanza, and Chick have invited many cell encapsulation groups from around the world to contribute to this book. The result is a very useful reference book in this rapidly growing area. With so many excellent au thors describing in detail the different areas of cell encapsulation, my role here will be to briefly discuss a few points.

Tapping into Unstructured Data

Tapping into Unstructured Data
Author : William H. Inmon,Anthony Nesavich
Publisher : Pearson Education
Release Date : 2007-12-11
Category : Business & Economics
Total pages :288
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The Definitive Guide to Unstructured Data Management and Analysis--From the World’s Leading Information Management Expert A wealth of invaluable information exists in unstructured textual form, but organizations have found it difficult or impossible to access and utilize it. This is changing rapidly: new approaches finally make it possible to glean useful knowledge from virtually any collection of unstructured data. William H. Inmon--the father of data warehousing--and Anthony Nesavich introduce the next data revolution: unstructured data management. Inmon and Nesavich cover all you need to know to make unstructured data work for your organization. You’ll learn how to bring it into your existing structured data environment, leverage existing analytical infrastructure, and implement textual analytic processing technologies to solve new problems and uncover new opportunities. Inmon and Nesavich introduce breakthrough techniques covered in no other book--including the powerful role of textual integration, new ways to integrate textual data into data warehouses, and new SQL techniques for reading and analyzing text. They also present five chapter-length, real-world case studies--demonstrating unstructured data at work in medical research, insurance, chemical manufacturing, contracting, and beyond. This book will be indispensable to every business and technical professional trying to make sense of a large body of unstructured text: managers, database designers, data modelers, DBAs, researchers, and end users alike. Coverage includes What unstructured data is, and how it differs from structured data First generation technology for handling unstructured data, from search engines to ECM--and its limitations Integrating text so it can be analyzed with a common, colloquial vocabulary: integration engines, ontologies, glossaries, and taxonomies Processing semistructured data: uncovering patterns, words, identifiers, and conflicts Novel processing opportunities that arise when text is freed from context Architecture and unstructured data: Data Warehousing 2.0 Building unstructured relational databases and linking them to structured data Visualizations and Self-Organizing Maps (SOMs), including Compudigm and Raptor solutions Capturing knowledge from spreadsheet data and email Implementing and managing metadata: data models, data quality, and more

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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Advances in Banking Technology and Management: Impacts of ICT and CRM

Advances in Banking Technology and Management: Impacts of ICT and CRM
Author : Ravi, Vadlamani
Publisher : IGI Global
Release Date : 2007-10-31
Category : Computers
Total pages :380
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Banking across the world has undergone extensive changes thanks to the profound influence of developments and trends in information communication technologies, business intelligence, and risk management strategies. While banking has become easier and more convenient for the consumer, the advances and intricacies of emerging technologies have made banking operations all the more cumbersome. Advances in Banking Technology and Management: Impacts of ICT and CRM examines the various myriads of technical and organizational elements that impact services management, business management, risk management, and customer relationship management, and offers research to aid the successful implementation of associated supportive technologies.

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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Trace Metal Concentrations in Marine Organisms

Trace Metal Concentrations in Marine Organisms
Author : Ronald Eisler
Publisher : Pergamon
Release Date : 1981
Category : Marine animals
Total pages :687
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Lowman.

Data Virtualization for Business Intelligence Systems

Data Virtualization for Business Intelligence Systems
Author : Rick van der Lans
Publisher : Elsevier
Release Date : 2012-07-25
Category : Computers
Total pages :296
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Data virtualization can help you accomplish your goals with more flexibility and agility. Learn what it is and how and why it should be used with Data Virtualization for Business Intelligence Systems. In this book, expert author Rick van der Lans explains how data virtualization servers work, what techniques to use to optimize access to various data sources and how these products can be applied in different projects. You’ll learn the difference is between this new form of data integration and older forms, such as ETL and replication, and gain a clear understanding of how data virtualization really works. Data Virtualization for Business Intelligence Systems outlines the advantages and disadvantages of data virtualization and illustrates how data virtualization should be applied in data warehouse environments. You’ll come away with a comprehensive understanding of how data virtualization will make data warehouse environments more flexible and how it make developing operational BI applications easier. Van der Lans also describes the relationship between data virtualization and related topics, such as master data management, governance, and information management, so you come away with a big-picture understanding as well as all the practical know-how you need to virtualize your data. First independent book on data virtualization that explains in a product-independent way how data virtualization technology works. Illustrates concepts using examples developed with commercially available products. Shows you how to solve common data integration challenges such as data quality, system interference, and overall performance by following practical guidelines on using data virtualization. Apply data virtualization right away with three chapters full of practical implementation guidance. Understand the big picture of data virtualization and its relationship with data governance and information management.

International Journal of Human Development and Sustainability Vol.4, No.1

International Journal of Human Development and Sustainability Vol.4, No.1
Author : Anonim
Publisher : Universal-Publishers
Release Date : 2011-09-28
Category :
Total pages :129
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