With this textbook, Vaisman and Zimanyi 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 conceptual and logical data warehouse design, as well as querying using MDX, DAX and SQL/OLAP. This part also covers data analytics using Power BI and Analysis Services. Part II details "Implementation and Deployment," including physical design, ETL and data warehouse design methodologies. Part III covers "Advanced Topics" and it is almost completely new in this second edition. This part includes chapters with an in-depth coverage of temporal, spatial, and mobility data warehousing. Graph data warehouses are also covered in detail using Neo4j. The last chapter extensively studies big data management and the usage of Hadoop, Spark, distributed, in-memory, columnar, NoSQL and NewSQL database systems, and data lakes in the context of analytical data processing. 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 Power BI. All chapters have been revised and updated to the latest versions of the software tools used. KPIs and Dashboards are now also developed using DAX and Power BI, and the chapter on ETL has been expanded with the implementation of ETL processes in PostgreSQL. Review questions and exercises complement each chapter to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available online and includes 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. "I can only invite you to dive into the contents of the book, feeling certain that once you have completed its reading (or maybe, targeted parts of it), you will join me in expressing our gratitude to Alejandro and Esteban, for providing such a comprehensive textbook for the field of data warehousing in the first place, and for keeping it up to date with the recent developments, in this current second edition." From the foreword by Panos Vassiliadis, University of Ioannina, Greece.

From environmental management to land planning and geo-marketing, the number of application domains that may greatly benefit from using data enriched with spatio-temporal features is expanding very rapidly. Unfortunately, development of new spatio-temporal applications is hampered by the lack of conceptual design methods suited to cope with the additional complexity of spatio-temporal data. This complexity is obviously due to the particular semantics of space and time, but also to the need for multiple representations of the same reality to address the diversity of requirements from highly heterogeneous user communities. Conceptual design methods are also needed to facilitate the exchange and reuse of existing data sets, a must in geographical data management due to the high collection costs of the data. Yet, current practice in areas like geographical information systems or moving objects databases does not include conceptual design methods very well, if at all.

This book shows that a conceptual design approach for spatio-temporal databases is both feasible and easy to apprehend. While providing a firm basis through extensive discussion of traditional data modeling concepts, the major focus of the book is on modeling spatial and temporal information. Parent, Spaccapietra and Zimanyi provide a detailed and comprehensive description of an approach that fills the gap between application conceptual requirements and system capabilities, covering both data modeling and data manipulation features. The ideas presented summarize several years of research on the characteristics and description of space, time, and perception. In addition to the authors' own data modeling approach, MADS (Modeling of Application Data with Spatio-temporal features), the book also surveys alternative data models and approaches (from industry and academia) that target support of spatio-temporal modeling.

The reader will acquire intimate knowledge of both the traditional and innovative features that form a consistent data modeling approach. Visual notations and examples are employed extensively to illustrate the use of the various constructs. Therefore, this book is of major importance and interest to advanced professionals, researchers, and graduate or post-graduate students in the areas of spatio-temporal databases and geographical information systems.

"For anyone thinking of doing research in this field, or who is developing a system based on spatio-temporal data, this text is essential reading." (Mike Worboys, U Maine, Orono, ME, USA)

"The high-level semantic model presented and validated in this book provides essential guidance to researchers and implementers when improving the capabilities of data systems to serve the actual needs of applications and their users in the temporal and spatial domains that are so prevalent today." (Gio Wiederhold, Stanford U, CA, USA)"

This exceptional work provides readers with an introduction to the state-of-the-art research on data warehouse design, with many references to more detailed sources. It offers a clear and a concise presentation of the major concepts and results in the subject area. Malinowski and Zim nyi explain conventional data warehouse design in detail, and additionally address two innovative domains recently introduced to extend the capabilities of data warehouse systems: namely, the management of spatial and temporal information.

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. The tutorials were given by renowned experts and covered advanced aspects of business intelligence and big data. This summer school, presented by leading researchers in the field, represented an opportunity for postgraduate students to equip themselves with the theoretical, practical, and collaboration skills necessary for developing challenging business intelligence applications.

This book constitutes the tutorial lectures of the 6th European Business Intelligence and Big Data Summer School, eBISS 2016, held in Tours, France, in July 2016. Tutorials were given by renowned experts and covered recent and various aspects of Business Intelligence and Big Data processing, including analytics on graph data, machine translation, pattern mining, scalability, and energy consumption. This volume contains the corresponding lecture notes of the summer school.

With this textbook, Vaisman and Zimanyi 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 the tutorial lectures of the 5th European Business Intelligence Summer School, eBISS 2015, held in Barcelona, Spain, in July 2015. The tutorials presented here in an extended and refined format were given by renowned experts and cover topics including schema evolution for databases and data warehouses, publishing OLAP cubes on the Semantic Web, design issues in social business intelligence projects, context-aware business intelligence, and key performance indicators in data warehouses.

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.

To large organizations, business intelligence (BI) promises the capability of collecting and analyzing internal and external data to generate knowledge and value, thus providing decision support at the strategic, tactical, and operational levels. BI is now impacted by the "Big Data" phenomena and the evolution of society and users. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors', suppliers', or distributors' data, governmental or NGO-based analysis and papers, or from research publications. In addition, they must be able to provide their results also on mobile devices, taking into account location-based or time-based environmental data.

The lectures held at the Third European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI and BPM technologies, but extend into innovative aspects that are important in this new environment and for novel applications, e.g., pattern and process mining, business semantics, Linked Open Data, and large-scale data management and analysis.

Combining papers by leading researchers in the field, this volume equips the reader with the state-of-the-art background necessary for creating the future of BI. It also provides the reader with an excellent basis and many pointers for further research in this growing field.

To large organizations, business intelligence (BI) promises the capability of collecting and analyzing internal and external data to generate knowledge and value, thus providing decision support at the strategic, tactical, and operational levels. BI is now impacted by the "Big Data" phenomena and the evolution of society and users. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors', suppliers', or distributors' data, governmental or NGO-based analysis and papers, or from research publications. In addition, they must be able to provide their results also on mobile devices, taking into account location-based or time-based environmental data.

The lectures held at the Second European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI and BPM technologies, but extend into innovative aspects that are important in this new environment and for novel applications, e.g., machine learning, logic networks, graph mining, business semantics, large-scale data management and analysis, and multicriteria and collaborative decision making.

Combining papers by leading researchers in the field, this volume equips the reader with the state-of-the-art background necessary for creating the future of BI. It also provides the reader with an excellent basis and many pointers for further research in this growing field.

This exceptional work provides readers with an introduction to the state-of-the-art research on data warehouse design, with many references to more detailed sources. It offers a clear and a concise presentation of the major concepts and results in the subject area. Malinowski and Zim nyi explain conventional data warehouse design in detail, and additionally address two innovative domains recently introduced to extend the capabilities of data warehouse systems: namely, the management of spatial and temporal information.

From environmental management to land planning and geo-marketing, the number of application domains that may greatly benefit from using data enriched with spatio-temporal features is expanding very rapidly. This book shows that a conceptual design approach for spatio-temporal databases is both feasible and easy to apprehend. While providing a firm basis through extensive discussion of traditional data modeling concepts, the major focus of the book is on modeling spatial and temporal information.

This book constitutes 5 revised tutorial lectures of the 9th European Business Intelligence and Big Data Summer School, eBISS 2019, held in Berlin, Germany, during June 30 - July 5, 2019. The tutorials were given by renowned experts and covered advanced aspects of business intelligence and big data. This summer school, presented by leading researchers in the field, represented an opportunity for postgraduate students to equip themselves with the theoretical and practical skills necessary for developing challenging business intelligence applications.

Business Intelligence (BI) promises an organization the capability of collecting and analyzing internal and external data to generate knowledge and value, providing decision support at the strategic, tactical, and operational levels. Business Intelligence is now impacted by the Big Data phenomena and the evolution of society and users, and needs to take into account high-level semantics, reasoning about unstructured and structured data, and to provide a simplified access and better understanding of diverse BI tools accessible trough mobile devices. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors', suppliers', or distributors' data, governmental or NGO-based analysis and papers, or from research publications. The lectures held at the First European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI technologies like data warehouses, OLAP query processing, or performance issues, but extend into new aspects that are important in this new environment and for novel applications, e.g., semantic technologies, social network analysis and graphs, services, large-scale management, or collaborative decision making. Combining papers by leading researchers in the field, this volume will equip the reader with the state-of-the-art background necessary for inventing the future of BI. It will also provide the reader with an excellent basis and many pointers for further research in this growing field.

Mobility of people and goods is essential in the global economy. The ability to track the routes and patterns associated with this mobility offers unprecedented opportunities for developing new, smarter applications in different domains. Much of the current research is devoted to developing concepts, models, and tools to comprehend mobility data and make it manageable for these applications. This book surveys the myriad facets of mobility data, from spatio-temporal data modeling, to data aggregation and warehousing, to data analysis, with a specific focus on monitoring people in motion (drivers, airplane passengers, crowds, and even animals in the wild). Written by a renowned group of worldwide experts, it presents a consistent framework that facilitates understanding of all these different facets, from basic definitions to state-of-the-art concepts and techniques, offering both researchers and professionals a thorough understanding of the applications and opportunities made possible by the development of mobility data.