 Chapter 1: This chapter offers a general introduction to Berkeley DB. It includes a brief history of its development and a basic description of its architecture. If you’re already familiar with Berkley DB, you can skip this chapter.
Chapter 2: This chapter explains the pros and cons of using Berkeley DB. If you’re evaluating Berkeley DB to decide whether to use it in your application, then you should read this chapter.
Chapter 3: This chapter describes the process of compiling and installing different Berkeley DB products. Experienced Berkeley DB users can safely skip this chapter.
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Read more about this free ebooks:The Berkeley DB Book:The EXPERT's VOIce in Open Source
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 Chapter I Metric Methods in Data Mining
This chapter presents data mining techniques that make use of metrics defined on the set of partitions of finite sets.
Chapter II Bi-Directional Constraint Pushing in Frequent Pattern Mining
Frequent itemset mining (FIM) is a key component of many algorithms that extract patterns from transactional databases.
Chapter III Mining Hyperclique Patterns: A Summary of Results
This chapter presents a framework for mining highly correlated association |
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Read more about this free ebooks:IGI Publishing-Data Mining Patterns: New Methods and Applications
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 The majority of this book, Chapters 6 through 11, consists of reference material that describes each refactoring in detail. The first five chapters describe the fundamental ideas and techniques of evolutionary database development, and in particular, database refactoring. You should read these chapters in order:
Chapter 1, "Evolutionary Database Development," overviews the fundamentals of evolutionary development and the techniques that support it. |
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Read more about this free ebooks:Refactoring Databases: Evolutionary Database Design
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 Section I, on Data Warehousing and Mining, consists of three chapters covering data mining techniques applied to data warehouse Web logs, data cubes, and highdimensional datasets.
Chapter.I, “Combining Data Warehousing and Data Mining Techniques for Web Log Analysis” There are several approaches to analyze Web logs. They propose a hybrid method that combines data warehouse Web log schemas and a data mining technique called Hyper Probabilistic Grammars, resulting in a fast and flexible Web log analysis.
Chapter.II, “Computing Dense Cubes Embedded in Sparse Data” |
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Read more about this free ebooks:Research and Trends in Data Mining Technologies and Applications
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 Chapter I System of Information Retrieval in XML Documents
This chapter introduces the process to retrieve units (or subdocuments) of relevant information from XML documents.
Chapter II Information Extraction from Free-Text Business Documents
The objective of this chapter is an investigation of the applicability of information extraction techniques in real-world business applications dealing with textual data since business relevant data is mainly transmitted through free-text documents.
Chapter III Interactive Indexing of Documents with a Multilingual Thesaurus |
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Read more about this free ebooks:Effective Databases For Text And Document Management
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