Нейросетевая технология представления и обработки информации
Под редакцией: А.И. Галушкина
Document Warehousing and Text Mining: Techniques for Improving Business Operations, Marketing and Sales
by Dan SullivanIn Document Warehousing and Text Mining, author Dan Sullivan provides a vision for the evolution of business intelligence systems. ... Simply put, those [companies] that understand and leverage the concepts presented can achieve superior business results."
Steve J. Vandehey
Partner, Baseline Consulting Group
Author of e-Data: Turning Data Into Information With Data Warehousing and The CRM Handbook
Document Warehousing and Text Mining addresses one of the most pressing problems facing today's organizations: how to deal with the overwhelming volumes of texts that contain vital operational information, business intelligence, market news, intellectual property and other knowledge-based resources.
The Problem: Effectively Managing Knowledge and Text
Organizations face numerous challenges in dealing with the overwhelming volumes of texts available in today's business environment:
- Saving text on file serves or document management systems can help to ensure that information is not lost but they are not adequate means of delivering targeted content to specific audiences. The Web is an unparalleled resource but its sheer volume creates barriers to effectively exploiting it to its full potential.
- Portals can deliver content but require information about the content' meaning as well as the user's interests.
- Document warehousing and text mining techniques are designed to address these problems and this book explains how to use them in your organization.
The Solution: Document Warehousing and Text Mining
Document Warehousing and Text Mining: Techniques for Improving Business Operations, Marketing and Sales begins with an explanation of the need to address the semantic content of text in an organized and systematic manner. It then shows that the common misconception of "unstructured text" is not only a myth, but that the richly structured nature of language allows us to tap the business intelligence and knowledge management potential of textual resources.
The second part of the book explains how to design and implement a document warehouse while drawing parallels to data warehouse implementations. The book is designed to help executives and project managers understand the nature of document warehousing but it doesn't stop there. The book also provides the concrete details, including code samples and data models, needed by developers to implement document warehouses.
Part three of Document Warehousing and Text Mining looks at the application of text mining techniques to real world business problems, including operations, customer relationship management and competitive intelligence. Again, concrete business problems are discussed and the solutions presented with enough detail so that developers can begin building applications immediately. The basic concepts underlying text mining and information retrieval are explained in detail and examples are provided using some of the best commercially available tools, including Megaputer's TextAnalyst (TM), Oracle interMedia Text (TM) and IBM Intelligent Miner for Text (TM).