This textbook covers the key topics in mobility data analysis, including all steps of the data science pipeline illustrated with real-world examples.

The book is composed of three parts. Part I “Fundamental Concepts” provides the background for this book by introducing spatial and temporal databases and motivating the need for mobility databases. Further chapters in this part are devoted to a formal model for representing mobility data, an introduction to mobility data visualization, and the topic of querying mobility databases. Part II “Advanced Topics” covers topics such as query processing and indexing, illustrated with PostgreSQL, introduces mobility data warehouses using synthetic data, and concludes with distributed mobility databases. Part III “Mobility Analytics” covers important topics like mobility data cleaning, including the identification of erroneous data, and mobility analysis using foundational algorithms for spatial and mobility data. It also includes an urban mobility use case that illustrates the concepts presented throughout the book in a real application setting.

This textbook is written for undergraduate and graduate computer science courses on mobility data science. As such, it follows a pedagogical style to make the work of the instructor easier and to help students to understand the concepts being delivered, complementing the presentation with exercises and a companion GitHub repository. SQL is used as a high-level language for analytics, allowing students to write complex data science code, while abstracting away implementation details. Researchers and practitioners who are interested in an introduction to the area of mobility data science will also find the book a useful reference.

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.

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 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 NewSQLdatabase 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.

This textbook covers the key topics in mobility data analysis, including all steps of the data science pipeline illustrated with real-world examples.

The book is composed of three parts. Part I “Fundamental Concepts” provides the background for this book by introducing spatial and temporal databases and motivating the need for mobility databases. Further chapters in this part are devoted to a formal model for representing mobility data, an introduction to mobility data visualization, and the topic of querying mobility databases. Part II “Advanced Topics” covers topics such as query processing and indexing, illustrated with PostgreSQL, introduces mobility data warehouses using synthetic data, and concludes with distributed mobility databases. Part III “Mobility Analytics” covers important topics like mobility data cleaning, including the identification of erroneous data, and mobility analysis using foundational algorithms for spatial and mobility data. It also includes an urban mobility use case that illustrates the concepts presented throughout the book in a real application setting.

This textbook is written for undergraduate and graduate computer science courses on mobility data science. As such, it follows a pedagogical style to make the work of the instructor easier and to help students to understand the concepts being delivered, complementing the presentation with exercises and a companion GitHub repository. SQL is used as a high-level language for analytics, allowing students to write complex data science code, while abstracting away implementation details. Researchers and practitioners who are interested in an introduction to the area of mobility data science will also find the book a useful reference.

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.

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 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 NewSQLdatabase 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.