Three years ago next month I posted on our first book and now we can peek at what's to come!

I guess with the price of gas, Wiley decided that black gold was more interesting than the yellow stuff.  Readers of the last edition will notice a new addition to the author's list - Bogdan has contributed greatly to this version of the book and has helped make it far superior to the previous version.  Many chapters have been completely rewritten or greatly extended based on reader feedback, new features, and also how we see the technology has evolved since we drafted the first edition.  Another big change, depending on how the publisher pulls it off, most chapters have DMX and project files on the "related web site."  Here's a draft excerpt from the "front matter" explaining the chapter list:

 

Chapter 1 - Introduction - The chapter you’re reading not only introduces the book, but also the technology. It continues with a detailed definition of what exactly is meant by the term “data mining” and discusses what kinds of problems are addressed by this technology.

Chapter 2 – Applied Data Mining Using Office 2007 – This chapter provides an overview of the Table Analysis Tools for Office 2007 – a rich set of tools for Excel that are usable by any information worker. This chapter explains how and why you use these tools and provides guidance on how to get the best results.

Chapter 3 – Data Mining Concepts and DMX – This chapter is critical to your understanding of the SQL Server Data Mining platform. It explains the underlying concepts of how you think about a data mining problem plus it details the Data Mining Extensions for SQL (DMX) in a learn-by-example framework.

Chapter 4 – Using the SQL Server Data Mining Toolset – This chapter introduces you to building data mining solutions using the Business Intelligence Development Studio (BIDS). Beyond a basic overview, it provides a wide range of tips and tricks that can make the difference between a successful project and a failed one. This chapter also covers using SQL Server Management Studio to access and secure data mining objects also includes a discussion on how to expose your data mining models through SQL Server Reporting Services.

Chapter 5 – Implementing a Data Mining Process Using Office 2007 – This chapter goes beyond the Table Analysis Tools described in chapter 2 and provides an alternative framework for implementing data mining solutions than BIDS/SQL Server Management Studio. This chapter explores the remaining addins of the Data Mining Addins for Office 2007 showing how they provide additional functionality than BIDS and SQL Server Management Studio while having limitations preventing them from exposing the full functionality of SQL Server Data Mining. In any case, this chapter will allow you to best take advantage of the Microsoft Office tools for data mining.

Chapters 6 – 12 The Algorithm Chapters – Each of these chapters is devoted to one or more of the algorithms included with SQL Server Data Mining. In each of the chapters you will find a basic description of the algorithm, followed by usage scenarios that will help you understand how, when, and where you apply each algorithm. Each chapter describes how you create, train, interpret, and apply models using the specified algorithms. In the end, for those who hunger for the details, the chapters wrap up with a deeper technical dive into how the algorithms work.

Chapter 13 – Mining OLAP Cubes – This chapter provides a brief introduction to OLAP and the OLAP functionality of SQL Server Analysis Services followed by an explanation of how and when you perform data mining on OLAP cubes. This chapter includes details on how to implement popular OLAP mining scenarios.

Chapter 14 – Data Mining with SQL Server Integration Services – This chapter introduces SQL Server Integration Services (SSIS) and describes its various components. It then details the tasks and transforms that you use to implement data mining solutions in your data integration packages. This chapter also introduces the text mining components that are used to prepare unstructured data for use in data mining scenarios.

Chapter 15 – Data Mining Architecture – This is the first chapter that moves away from tools and concepts and starts to delve in to the programming and administration aspects of SQL Server Data Mining. This chapter discusses the architecture of a server-based data mining system including introducing the XML for Analysis (XMLA) protocol that underlies all client-server communication. This chapter also discusses administering a data mining server including server properties that are important for SQL Server Data Mining and data mining security roles.

Chapter 16 – Programming SQL Server Data Mining – This chapter details the programming interfaces for SQL Server Data Mining and includes many examples on the programmatic creation, training, and application of data mining objects.

Chapter 17 – Extending SQL Server Data Mining – This chapter shows how you can extend SQL Server Data Mining with your own functionality. It describes how you can create stored procedures to add additional operations to DMX, how you can implement your own data mining algorithms to plug into SQL Server Data Mining and exploit all of the SQL Server Data Mining features and integration, and how you can write your own data mining visualizations to display patterns in either the supplied algorithms or your own algorithm implementations and embed them in BIDS and SQL Server Management Studio.

Chapter 18 Implementing a Web Cross-Selling Application – This chapter walks you through a common data mining scenario – implementing a recommendation engine and integrating it into a retail web site. It includes sample queries and code to get you started.

The book is available for preorder at Amazon - order 10 copies!