Scale is really a great concern for many information and data processing technologies. In the research area of data warehousing, knowledge discovery, and data mining, how to deal with large scale and complex data is an important issue of interest to many researchers. This book is a collection of papers intended to include current research about methods and applications in complex data warehousing and knowledge discovery for advanced retrieval development. Accordingly, the main market for this collection, are experts in the area of data warehousing and data mining.
This book contains 16 chapters organised into five sections. The first section contains three chapters on data warehousing architectures and fundamentals. Chapter 1 proposes an LBF R‐tree framework‐based scalable indexing and storage for data warehousing systems, while chapter 2 addresses a performance issue in dynamic workload for schema evolution in data warehousing. The last chapter in this section presents an optimising view of materialisation cost in spatial data warehousing.
Section 2 consists of three chapters on multidimensional data and OLAP. Chapter 4 presents an integrating and preserving decision‐maker's expertise in multidimensional systems based on decisional annotation. Chapter 5 discusses federated data warehouses, and chapter 6 introduces an algorithm for built‐in indicators to support business intelligence in OLAP databases.
There are three chapters in section 3 related to the issue of data warehousing and OLAP applications. Chapter 7 presents a conceptual data warehouse design methodology for business process intelligence, and chapter 8 describes an application for data warehouse facilitating evidence‐based medicine. Chapter 9 discusses deploying data warehouses in grids with efficiency and availability.
The last two sections shift the focus from data warehousing to data mining. Section 4 contains three chapters about data mining techniques, and section 5 includes four papers in the research field of advanced mining applications. Chapter 10 introduces MOSAIC, a technique for agglomerative clustering with Gabriel graphs, and chapter 11 discusses the ranking of gradients in multi‐dimensional spaces. Chapter 12 proposes a methodology to combine simultaneous feature selection and Tuple selection for efficient classification.
In section 5, chapter 13 applies cost‐sensitive decision trees to support medical diagnosis. The remaining three chapters are all related to data streams. Chapter 14 proposes an approximate approach for maintaining recent occurrences of item sets in a sliding window over data streams, and chapter 15 presents an approach for protocol identification of encrypted network streams. Chapter 16 describes a technique for exploring calendar‐based pattern mining in data streams.
All chapters contain references for further reading, and, a compilation of the references, are also provided in an appendix. In addition, the book provides a clear and detailed table of contents, which allows the reader to understand the structure of the book. This collection is recommended to those researchers who are experts in the fields of data warehousing and data mining as well as to those readers who are interested in understanding how data warehousing and data mining can be applied, especially for complex and large amounts of data.
