Analyzing Application Service Providers

Author: Alexander Factor

Publisher: Prentice Hall Professional

ISBN: 9780130894250

Category: Computers

Page: 326

View: 8951

An enterprising Computing series title that focuses on the power of ISD to increase productivity by defining scope of services and responsibilities throughout the IT Enterprise.
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Building, Using, and Managing the Data Warehouse

Author: Ramón C. Barquín,Herb Edelstein

Publisher: Prentice Hall

ISBN: 9780135343555

Category: Computers

Page: 317

View: 4052

When it comes to making organizations smarter, faster, and more competitive, few technologies have more promise than data warehousing. This book shows you how to translate that promise into reality.
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Transformation operativer Daten zur Nutzung im Data Warehouse

Author: Jochen Müller

Publisher: Springer-Verlag

ISBN: 3663090523

Category: Computers

Page: 275

View: 7250

Jochen Müller vergleicht charakteristische Merkmale operativer und analyseorientierter Informationssysteme und untersucht die besondere Bedeutung der Schnittstellen zu den datenliefernden Vorsystemen.
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Building the Operational Data Store

Author: W. H. Inmon

Publisher: Wiley

ISBN: 9780471328889

Category: Computers

Page: 336

View: 9904

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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Corporate Information Factory

Author: W. H. Inmon,Claudia Imhoff,Ryan Sousa

Publisher: John Wiley & Sons

ISBN: 0471437506

Category: Computers

Page: 400

View: 3552

The "father of data warehousing" incorporates the latesttechnologies into his blueprint for integrated decision supportsystems Today's corporate IT and data warehouse managers are required tomake a small army of technologies work together to ensure fast andaccurate information for business managers. Bill Inmon created theCorporate Information Factory to solve the needs ofthese managers. Since the First Edition, the design of the factoryhas grown and changed dramatically. This Second Edition, revisedand expanded by 40% with five new chapters, incorporates thesechanges. This step-by-step guide will enable readers to connecttheir legacy systems with the data warehouse and deal with a hostof new and changing technologies, including Web access mechanisms,e-commerce systems, ERP (Enterprise Resource Planning) systems. Thebook also looks closely at exploration and data mining servers foranalyzing customer behavior and departmental data marts forfinance, sales, and marketing.
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Building a Data Warehouse

With Examples in SQL Server

Author: Vincent Rainardi

Publisher: Apress

ISBN: 9781590599310

Category: Computers

Page: 523

View: 7823

Building a Data Warehouse: With Examples in SQL Server describes how to build a data warehouse completely from scratch and shows practical examples on how to do it. Author Vincent Rainardi also describes some practical issues he has experienced that developers are likely to encounter in their first data warehousing project, along with solutions and advice. The relational database management system (RDBMS) used in the examples is SQL Server; the version will not be an issue as long as the user has SQL Server 2005 or later. The book is organized as follows. In the beginning of this book (chapters 1 through 6), you learn how to build a data warehouse, for example, defining the architecture, understanding the methodology, gathering the requirements, designing the data models, and creating the databases. Then in chapters 7 through 10, you learn how to populate the data warehouse, for example, extracting from source systems, loading the data stores, maintaining data quality, and utilizing the metadata. After you populate the data warehouse, in chapters 11 through 15, you explore 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. Chapters 16 and 17 wrap up the book: After you have built your data warehouse, before it can be released to production, you need to test it thoroughly. After your application is in production, you need to understand how to administer data warehouse operation. What you’ll learn A detailed understanding of what it takes to build a data warehouse The implementation code in SQL Server to build the data warehouse Dimensional modeling, data extraction methods, data warehouse loading, populating dimension and fact tables, data quality, data warehouse architecture, and database design Practical data warehousing applications such as business intelligence reports, analytics applications, and customer relationship management Who this book is for There are three audiences for the book. The first are the people who implement the data warehouse. This could be considered a field guide for them. The second is database users/admins who want to get a good understanding of what it would take to build a data warehouse. Finally, the third audience is managers who must make decisions about aspects of the data warehousing task before them and use the book to learn about these issues.
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Analytische Informationssysteme

Data Warehouse, On-Line Analytical Processing, Data Mining

Author: Peter Chamoni,Peter Gluchowski

Publisher: Springer-Verlag

ISBN: 3662057107

Category: Computers

Page: 484

View: 5476

Neben den operativen Informationssystemen, welche die Abwicklung des betrieblichen Tagesgeschäftes unterstützen, treten heute verstärkt Informationssysteme für analytische Aufgaben der Fach- und Führungskräfte in den Vordergrund. In fast allen Unternehmen werden derzeit Begriffe und Konzepte wie Data Warehouse, On-Line Analytical Processing und Data Mining diskutiert und die zugehörigen Produkte evaluiert. Vor diesem Hintergrund zielt der vorliegende Sammelband darauf ab, einen aktuellen Überblick über Technologien, Produkte und Trends zu bieten. Als Entscheidungsgrundlage für den Praktiker beim Aufbau und Einsatz derartiger analytischer Informationssysteme können die unterschiedlichen Beiträge aus Wirtschaft und Wissenschaft wertvolle Hilfestellung leisten.
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Managing Information in the Public Sector

Author: Jay D. White

Publisher: M.E. Sharpe

ISBN: 9780765630216

Category: Political Science

Page: 336

View: 5809

This first-of-its-kind survey covers both the basics of information technology and the managerial and political issues surrounding the use of these technologies. Unlike other works on information systems, this book is written specifically for the public sector and addresses unique public sector issues and concerns. The technical basics are explained in clear English with as little technical jargon as possible so that readers can move on to informed analysis of the public policy issues surrounding government's use of MIS. This practical tool includes end of chapter summaries with bridges to upcoming chapters, numerous boxed exhibits, thorough end-of-chapter notes and a bibliography for further reading.
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Vom Data Warehouse zum Corporate Knowledge Center

Proceedings der Data Warehousing 2002

Author: Eitel Maur,Robert Winter

Publisher: Springer-Verlag

ISBN: 3642574912

Category: Computers

Page: 531

View: 7061

Das Buch Vom Data Warehouse zum Corporate Knowledge Center vermittelt einen tiefen Einblick in den State-of-the-Art sowie die Zukunftsperspektiven im Bereich der integrierten Informationslogistik. Hierbei wird zum einen betrachtet, inwieweit sich bisherige Ansätze zum Data Warehousing mittelfristig technisch, organisatorisch und wirtschaftlich als geeignete Lösungen erwiesen haben. Die Kernthemen hierbei sind Architekturen, Vorgehensmodelle, BI, OLAP und DSS/EIS. Zum anderen werden neuere Ansätze vorgestellt, die die Integration des Data Warehouse in die Gesamt-Informationslogistik zum Ziel haben und so die Realisierung neuer Applikationstypen und Geschäftsmodelle ermöglichen. Hierbei werden insbesondere die Themenbereiche CRM und Metadatenmanagement angesprochen.
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Building Big Data and Analytics Solutions in the Cloud

Author: Wei-Dong Zhu,Manav Gupta,Ven Kumar,Sujatha Perepa,Arvind Sathi,Craig Statchuk,IBM Redbooks

Publisher: IBM Redbooks

ISBN: 0738453994

Category: Computers

Page: 101

View: 9492

Big data is currently one of the most critical emerging technologies. Organizations around the world are looking to exploit the explosive growth of data to unlock previously hidden insights in the hope of creating new revenue streams, gaining operational efficiencies, and obtaining greater understanding of customer needs. It is important to think of big data and analytics together. Big data is the term used to describe the recent explosion of different types of data from disparate sources. Analytics is about examining data to derive interesting and relevant trends and patterns, which can be used to inform decisions, optimize processes, and even drive new business models. With today's deluge of data comes the problems of processing that data, obtaining the correct skills to manage and analyze that data, and establishing rules to govern the data's use and distribution. The big data technology stack is ever growing and sometimes confusing, even more so when we add the complexities of setting up big data environments with large up-front investments. Cloud computing seems to be a perfect vehicle for hosting big data workloads. However, working on big data in the cloud brings its own challenge of reconciling two contradictory design principles. Cloud computing is based on the concepts of consolidation and resource pooling, but big data systems (such as Hadoop) are built on the shared nothing principle, where each node is independent and self-sufficient. A solution architecture that can allow these mutually exclusive principles to coexist is required to truly exploit the elasticity and ease-of-use of cloud computing for big data environments. This IBM® RedpaperTM publication is aimed at chief architects, line-of-business executives, and CIOs to provide an understanding of the cloud-related challenges they face and give prescriptive guidance for how to realize the benefits of big data solutions quickly and cost-effectively.
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Building the operational data store

Author: William H. Inmon,Claudia Imhoff,Greg Battas

Publisher: John Wiley & Sons Inc

ISBN: 9780471128229

Category: Business & Economics

Page: 276

View: 3256

The operational data store takes what the data warehouse accomplishes at the strategic and managerial level and applies it to the operational level. W.H. Inmon--the "father" of the data warehouse--and his co-authors show you what the operational data store is, what it can do for you, and how to put it to work in your organization.
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Building and Managing Effective Physician Organizations Under Capitation

Author: Douglas E. Goldstein

Publisher: Jones & Bartlett Learning

ISBN: 9780834208094

Category: Business & Economics

Page: 363

View: 8440

This resource offers you a unique Building Block system, a proven-effective tool used by organizations to survive and prosper in an era of different reimbursement schemes, from discounted fee-for-service and primary care capitation, to global capitation and percent of premium payment.
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Mgmt Info Sys: Text & Cases

Author: Jawadekar

Publisher: Tata McGraw-Hill Education

ISBN: 9780070146624

Category: Management information systems

Page: 829

View: 1821

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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: 2976

"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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The Microsoft Data Warehouse Toolkit

With SQL Server 2008 R2 and the Microsoft Business Intelligence Toolset

Author: Joy Mundy,Warren Thornthwaite

Publisher: John Wiley & Sons

ISBN: 9781118067956

Category: Computers

Page: 704

View: 2141

Best practices and invaluable advice from world-renowned data warehouse experts In this book, leading data warehouse experts from the Kimball Group share best practices for using the upcoming “Business Intelligence release” of SQL Server, referred to as SQL Server 2008 R2. In this new edition, the authors explain how SQL Server 2008 R2 provides a collection of powerful new tools that extend the power of its BI toolset to Excel and SharePoint users and they show how to use SQL Server to build a successful data warehouse that supports the business intelligence requirements that are common to most organizations. Covering the complete suite of data warehousing and BI tools that are part of SQL Server 2008 R2, as well as Microsoft Office, the authors walk you through a full project lifecycle, including design, development, deployment and maintenance. Features more than 50 percent new and revised material that covers the rich new feature set of the SQL Server 2008 R2 release, as well as the Office 2010 release Includes brand new content that focuses on PowerPivot for Excel and SharePoint, Master Data Services, and discusses updated capabilities of SQL Server Analysis, Integration, and Reporting Services Shares detailed case examples that clearly illustrate how to best apply the techniques described in the book The accompanying Web site contains all code samples as well as the sample database used throughout the case studies The Microsoft Data Warehouse Toolkit, Second Edition provides you with the knowledge of how and when to use BI tools such as Analysis Services and Integration Services to accomplish your most essential data warehousing tasks.
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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: 3520

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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Building and Managing the Meta Data Repository

A Full Lifecycle Guide

Author: David Marco

Publisher: Wiley

ISBN: 9780471355236

Category: Computers

Page: 416

View: 8375

"This is the first book to tackle the subject of meta data in data warehousing, and the results are spectacular . . . David Marco has written about the subject in a way that is approachable, practical, and immediately useful. Building and Managing the Meta Data Repository: A Full Lifecycle Guide is an excellent resource for any IT professional." -Steve Murchie Group Product Manager, Microsoft Corporation Meta data repositories can provide your company with tremendous value if they are used properly and if you understand what they can, and can't, do. Written by David Marco, the industry's leading authority on meta data and well-known columnist for DM Review, this book offers all the guidance you'll need for developing, deploying, and managing a meta data repository to gain a competitive advantage. After illustrating the fundamental concepts, Marco shows you how to use meta data to increase your company's revenue and decrease expenses. You'll find a comprehensive look at the major trends affecting the meta data industry, as well as steps on how to build a repository that is flexible enough to adapt to future changes. This vendor-neutral guide alsoincludes complete coverage of meta data sources, standards, and architecture, and it explores the full gamut of practical implementation issues.Taking you step-by-step through the process of implementing a meta data repository, Marco shows you how to: - Evaluate meta data tools Build the meta data project plan - Design a custom meta data architecture - Staff a repository team - Implement data quality through meta data - Create a physical meta data model - Evaluate meta data delivery requirements The CD-ROM includes: - A sample implementation project plan - A function and feature checklist of meta data tool requirements - Several physical meta datamodels to support specific business functions Visit our Web site at www.wiley.com/compbooks/ Visit the companion Web site at www.wiley.com/compbooks/marco
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Data Architecture: A Primer for the Data Scientist

Big Data, Data Warehouse and Data Vault

Author: W.H. Inmon,Dan Linstedt

Publisher: Morgan Kaufmann

ISBN: 0128020911

Category: Computers

Page: 378

View: 5582

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
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