Oracle9IR2 Data Warehousing is a guide to using the Data Warehouse features in the latest versions of Oracle—Oracle9i Release 1 and Release 2. Written by people on the Oracle development team that designed and implemented the code, this updated and expanded edition provides an insider’s view of how the Oracle9i Release 2...

• Discover the Dimensional Modeling concept while designing a data warehouse
• Learn Data Movement based on technologies such as SSIS, MDS, and DQS
• ...

In the last three decades, machine learning research and practice have focused on batch learning usually using small datasets. In batch learning, the whole training data is available to the algorithm, which outputs a decision model after processing the data eventually (or most of the times) multiple times. The rationale behind this...

In my position at IBM, I regularly brief executives, managers, and computer professionals on data mining and neural network technology. In my briefings, I cover the fundamentals of data mining and neural networks, and I also discuss specific applications relevant to the customers' businesses. Since time is usually limited, my goal...

"Web Crawling and Data...

With today's increasing economic uncertainties, a strong investment strategy is to put a portion of your net worth in gold. However, given investors' overall lack of knowledge about gold as an investment, as wealth insurance, or as a store of...

Data Analysis Using SQL and Excel, 2nd Edition shows youhow to leverage the two most popular tools for data query andanalysis—SQL and Excel—to perform sophisticated dataanalysis without the need for complex and expensive data miningtools. Written by a...

Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining...

Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining...