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Tapping into Unstructured Data: Integrating Unstructured Data and Textual Analytics into Business Intelligence (Englisch) Taschenbuch – 21. Dezember 2007

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"The authors, the best minds on the topic, are breaking new ground. They show how every organization can realize the benefits of a system that can search and present complex ideas or data from what has been a mostly untapped source of raw data." --Randy Chalfant, CTO, Sun Microsystems 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 William H. Inmon is founder, president, and CTO of Inmon Data Systems. He is the father of the data warehouse concept, the corporate information factory, and the government information factory. Inmon has written 47 books on data warehouse, database, and information technology management; as well as more than 750 articles for trade journals such as Data Management Review, Byte, Datamation, and ComputerWorld. His b-eye-network.com newsletter currently reaches 55,000 people. Anthony Nesavich worked at Inmon Data Systems, where he developed multiple reports that successfully query unstructured data.Preface xvii 1 Unstructured Textual Data in the Organization 1 2 The Environments of Structured Data and Unstructured Data 15 3 First Generation Textual Analytics 33 4 Integrating Unstructured Text into the Structured Environment 47 5 Semistructured Data 73 6 Architecture and Textual Analytics 83 7 The Unstructured Database 95 8 Analyzing a Combination of Unstructured Data and Structured Data 113 9 Analyzing Text Through Visualization 127 10 Spreadsheets and Email 135 11 Metadata in Unstructured Data 147 12 A Methodology for Textual Analytics 163 13 Merging Unstructured Databases into the Data Warehouse 175 14 Using SQL to Analyze Text 185 15 Case Study--Textual Analytics in Medical Research 195 16 Case Study--A Database for Harmful Chemicals 203 17 Case Study--Managing Contracts Through an Unstructured Database 209 18 Case Study--Creating a Corporate Taxonomy (Glossary) 215 19 Case Study--Insurance Claims 219 Glossary 227 Index 233

Über den Autor und weitere Mitwirkende

Bill Inmon--the "father of data warehousing"--has written 50 books and published in nine languages on subjects such as data warehousing, database design, and architecture. For current events, seminars, conference speaking schedules, and a lot of other information related to data warehousing, unstructured data, and textual ETL, take a look at Bill Inmon's Web site at www.inmoncif.com. Anthony aka "Tony" Nesavich received his master's degree in computer information technology from Regis University in Denver, Colorado. He worked with Bill Inmon at Inmon Data Systems (IDS) where he was instrumental in the development of the IDS Foundation software. Much of Tony's contributions to IDS are discussed in this book. Tony lives in Denver, Colorado, with his wife Melissa and his faithful dog, Lola.

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(English below)

Als Einstiegsliteratur, um einen groben Überblick über den aktuellen Data Warehousing / Big / Unstructured Data-Hype zu bekommen, ist es ganz okay, aber es geht wenig ins Detail und enthält Fehler, die vermeidbar gewesen wären. Wenn man ein Buch zum Thema 'unstrukturierte Daten' macht und sich dabei v.a. auf unstrukturierte Textdaten konzentriert, sollte man vielleicht mal einen Computerlinguisten drüberlesen lassen. Der könnte einem dann auch sagen, dass 'Stemming' ([...]) keineswegs nur Wörter auf ihre 'lateinischen' Wurzeln zurückführt, sondern dass *alle* Wörter auf ihre Wurzeln / Wortstämme zurückgeführt werden, egal ob sie lateinischer Herkunft sind. Das Englische hat zwar viele Wörter mit lateinischen Wurzeln, aber Stemming behandelt definitiv nicht nur die... Peinlich!

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Okay as an entry-level reading and for getting an overview of the current data warehousing / big / unstructured data hype, but too little detail, and there are embarrassing mistakes which could have been avoided. If you're writing a book on unstructured data, with a heavy focus on textual data, maybe you should have it proof-read by a computational linguist - who could tell you, for instance, that stemming (http://en.wikipedia.org/wiki/Stemming) does *not* mean 'reducing words to their latinate roots', because 1) not all English words have latinate roots and 2) not only those with latinate roots are affected by stemming - all words are reduced to their roots, whether latinate or not. Embarrassing!
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Ich habe einige Jahre im IR- bzw. NLP-Umfeld gearbeitet und dadurch schnell erkannt, dass es da enorme Parallelen zum BI/DWH-Bereich gibt, mit dem Unterschied, dass man textuelle Daten verarbeitet und analysiert. Dieses Buch gibt eine Einführung in diese Thematik, aber auch nicht mehr! Es handelt sich nicht um ein Werk, das ausgiebig und auf akademischen Niveau die Thematik behandelt. Fazit, kann man als Einführungsbuch verwenden, aber nicht für viel mehr. Hätte mir eigentlich mehr erhofft...
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This book ist good at giving some direction in integrating the unstructured data into a data warehouse. The first chapters are strong, because they explain very well the diferrence between structured and unstructured data. Also categorization of unstructed data, like type of language "formal/informal", jargon, Repeated data, Updated is a useful way to cluster unstructured data.

However the approach to integrate unstructured data is pretty poor. The example in the book ar so simple, so that the reader might think: why should I do this integration? Where is the benefit?

The chapter 10, "Spreadsheets and Emails" does not bring any value to integration. It describes some situation on customer sites, but it doesn't make any valuable proposal, how to integrate them.

The book is missing references or chapters dedicated to RDF or ECM (Enterprise Content Management) which are essential in integrating unstructured data.

Generally speaking, it describe some aspects of the unstructured data, make some very high level of integrating the unstructured data, but doesn't go that deep, so that reader really know what they have to do.

It seems that the authors are good theoreticians, but they lack practice in working with unstructured data.
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Amazon.com: 2.5 von 5 Sternen 4 Rezensionen
12 von 13 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen High-Level Description of Text Analysis 1. Dezember 2010
Von John M. Ford - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
William Inman and Anthony Nesavich introduce the concepts of text analysis. They build on readers' familiarity with analysis of structured data from spreadsheets and databases. They describe how to transform text data into numbers and categories that can be analyzed with these traditional methods.

The book's chapters are of two types. The first fourteen chapters review the nature of business intelligence, discuss the challenges of analyzing structured and unstructured data, and lay out a general process for organizing and categorizing text data. Some sections are particularly good. Chapter 1, for example, suggests where useful unstructured data can be found in a typical organization. This is helpful guidance for an new analyst. Chapter 12 develops the framework of "A Methodology For Textual Analytics" that covers some of the key issues. This chapter contains most of the book's new information about text analysis.

The last five chapters present business intelligence case studies which used text analysis. The settings include conducting medical research, monitoring toxic chemicals, managing contract documents, creating a common corporate vocabulary, and imposing consistency on insurance claims.

The book has two weaknesses. First, it comes too slowly to its core material about text analysis. Early chapters review well-established business and data management practices to excess. There is far too much agonizing over whether to integrate unstructured data with structured data or analyze it separately. The second and more serious weakness is the abstract and almost cursory description of text analysis techniques. This is certainly not a technical tutorial. It is also not an adequate high-level description of the challenges and variations in such projects. It doesn't tell you how to fix the potholes or drive around them.

Read chapters 1 and 12 of this book for a quick overview of text analysis. Then try something with more depth, such as Svenja Adolphs' Introducing Electronic Text Analysis. For more detailed technical guidance, try Weis, Indurkhya and Zhang's Fundamentals of Predictive Text Mining.
3 von 3 Kunden fanden die folgende Rezension hilfreich
1.0 von 5 Sternen He might have got it wrong 30. September 2012
Von aussiejim - Veröffentlicht auf Amazon.com
Format: Kindle Edition Verifizierter Kauf
Like many of his earlier books Bill tends to generalise a little and although it all seems to make sense some of the thoughts are becoming outdated.
In this book Bill suggest to bring unstructured data INTO the warehouse but the current trend is to dump unstructured data into nosql databases with little or no modelling and apply statistical analysis to this BIG data. Then someone might wish to then test what they are infering by taking structured data out of the warehouse to compare or what if etc

Thus it seems to be going the other way, traditional data warehouses are taking the back seat to emerging big data technologies like hadoop, mapreduce, base, no sql, yada, yada, yada etc
People wouldnt dream of dumping all that data into a traditional warehouse IMHO
1 von 1 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen nothing insightful, practical or new - don't waste your time 13. Mai 2015
Von Ruchika - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
Seems like the author is much more focused on traditional databases than really understanding and explaining how unstructured data can be harnessed. The book repeats same things in each chapter, the case studies are very shallow and there are no practical exercises whatsoever. All in all - skip it. You will not learn much from this book.
1 von 16 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Good Book 27. Januar 2009
Von J. Wood - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
I need this book for my Information Quality course. It seems to be a good book so far. Easy to read.
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