Esteban Zimányi Knihy

With this textbook, Vaisman and Zimányi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes “Fundamental Concepts” including multi-dimensional models; conceptual and logical data warehouse design and MDX and SQL/OLAP. Subsequently, Part II details “Implementation and Deployment,” which includes physical data warehouse design; data extraction, transformation, and loading (ETL) and data analytics. Lastly, Part III covers “Advanced Topics” such as spatial data warehouses; trajectory data warehouses; semantic technologies in data warehouses and novel technologies like Map Reduce, column-store databases and in-memory databases. As a key characteristic of the book, most of the topics are presented and illustrated using application tools. Specifically, a case study based on the well-known Northwind database illustrates how the concepts presented in the book can be implemented using Microsoft Analysis Services and Pentaho Business Analytics. All chapters are summarized using review questions and exercises to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available at http://cs. ulb. ac. be/DWSDIbook/, including electronic versions of the figures, solutions to all exercises, and a set of slides accompanying each chapter. Overall, students, practitioners and researchers alike will find this book the most comprehensive reference work on data warehouses, with key topics described in a clear and educational style.

This book constitutes revised tutorial lectures of the 7th European Business Intelligence and Big Data Summer School, eBISS 2017, held in Bruxelles, Belgium, in July 2017.

A data warehouse is essential for storing large volumes of historical data for analytical purposes, utilizing the extraction-transformation-loading (ETL) process to integrate data from operational databases into a coherent multidimensional model. Malinowski and Zimányi provide an in-depth exploration of conventional data warehouse design, focusing on complex hierarchy modeling. They also introduce innovative domains that enhance data warehouse systems, specifically the management of spatial and temporal information. The authors outline various phases of the design process, including requirements specification, and detail three approaches for gathering requirements based on user input or operational data sources, leading to the creation of a conceptual multidimensional model. The concepts are illustrated with real-world examples and sample implementations for Microsoft’s Analysis Services 2005 and Oracle 10g, including OLAP and Spatial extensions. This work serves as a comprehensive introduction to contemporary data warehouse design for researchers, with numerous references for further exploration. It also provides a clear foundation for graduate or advanced undergraduate courses, aiding experienced designers in expanding their analytical capabilities with spatial and temporal data. Additionally, experts in spatial databases or geographical information systems can leverage the data warehouse perspective for innovative spatial a

The increasing application of data enriched with spatio-temporal features spans various domains, including environmental management and geo-marketing. However, the development of new spatio-temporal applications faces challenges due to a lack of conceptual design methods that can handle the complexity of spatio-temporal data. This complexity arises from the unique semantics of space and time and the necessity for multiple representations to meet the diverse needs of heterogeneous user communities. Effective conceptual design methods are crucial for facilitating data exchange and reuse, especially in geographical data management where collection costs are high. Unfortunately, current practices in geographical information systems and moving objects databases often overlook these methods. This book demonstrates that a conceptual design approach for spatio-temporal databases is both feasible and accessible. It provides a solid foundation through an extensive discussion of traditional data modeling concepts while focusing on modeling spatial and temporal information. The authors present a comprehensive description of their approach, MADS (Modeling of Application Data with Spatio-temporal features), and survey alternative models from both industry and academia. The text is rich with visual notations and examples, making it invaluable for advanced professionals, researchers, and students in spatio-temporal databases and geographical

This book constitutes the tutorial lectures of the 4th European Business Intelligence Summer School, eBISS 2014, held in Berlin, Germany, in July 2014. The tutorials presented here in an extended and refined format were given by renowned experts and cover topics including requirements engineering for decision-support systems, visual analytics of large data sets, linked data and semantic technologies, supervised classification on data streams, and knowledge reuse in large organizations.