Preface Chapter 2, " Dynamic Workload for Schema Evolution in Data Warehouses: a Performance Issue, "

Data warehousing and knowledge discovery are established key technologies in many application domains. Enterprises and organizations improve their abilities in data analysis, decision support, and the automatic extraction of knowledge from data; for scientific applications to analyze collected data, for medical applications for quality assurance and for steps to individualized medicine, to mention just a few examples. With the exponentially growing amount of information to be included in the decision making process, the data to be processed become more and more complex in both structure and semantics. Consequently, the process of retrieval and knowledge discovery from this huge amount of heterogeneous complex data constitutes the reality check for research in the area. The primary objective of the book Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications is to give readers comprehensive knowledge on the current issues as well as the development trends of data warehousing and knowledge discovery. The book presents the latest research issues and experiences in developing and deploying data warehousing and knowledge discovery systems, applications, and solutions. The 16 book chapters, organized in 5 Sections, were contributed by authors and editorial board members from the International Journal of Data Warehousing and Mining, as well as invited authors who are experts in the data warehousing and knowledge discovery field. Three chapters in Section 1, Data Warehouse Architectures & Fundamentals, present the current trends of research on data warehouse architecture, storage and implementations towards improving performance and response time. the authors propose an LBF R-tree framework for effective indexing mechanisms in multi-dimensional database environments. The proposed framework addressed not only improves performance on common user-defined range queries, but also gracefully degrades to a linear scan of the data on pathologically large queries. Experimental results demonstrating both efficient disk access on the LBF R-tree, as well as impressive compression ratios for data and indexes. addresses the issues related to the workload's evolution and maintenance in data warehouse systems in response to new requirements modeling resulting from users' personalized analysis needs. The proposed workload management system assists the administrator to maintain and adapt dynamically the work-load according to changes arising on the data warehouse schema by improving two types of workload updates: (1) maintaining existing queries consistent with respect to the new data warehouse schema and (2) creating new queries based on the new dimension hierarchy levels.