Esteban Zimanyi – författare

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.

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.

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.

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 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.

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.  

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 Zimányi 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 thetraditional 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)

A data warehouse stores large volumes of historical data required for analytical purposes. This data is extracted from operational databases; transformed into a coherent whole using a multidimensional model that includes measures, dimensions, and hierarchies; and loaded into a data warehouse during the extraction-transformation-loading (ETL) process.

Malinowski and Zimányi explain in detail conventional data warehouse design, covering in particular complex hierarchy modeling. Additionally, they address two innovative domains recently introduced to extend the capabilities of data warehouse systems, namely the management of spatial and temporal information. Their presentation covers different phases of the design process, such as requirements specification, conceptual, logical, and physical design. They include three different approaches for requirements specification depending on whether users, operational data sources, or both are the driving force in the requirements gathering process, and they show how each approach leads to the creation of a conceptual multidimensional model. Throughout the book the concepts are illustrated using many real-world examples and completed by sample implementations for Microsoft''s Analysis Services 2005 and Oracle 10g with the OLAP and the Spatial extensions.

For researchers this book serves as an introduction to the state of the art on data warehouse design, with many references to more detailed sources. Providing a clear and a concise presentation of the major concepts and results of data warehouse design, it can also be used as the basis of a graduate or advanced undergraduate course. The book may help experienced data warehouse designers to enlarge their analysis possibilities by incorporating spatial and temporal information. Finally, experts in spatial databases or in geographical information systems could benefit from the data warehouse vision for building innovative spatial analytical 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.