Big Data Management and Analytics

Advanced analytics, distilled from a summer school

Big Data Management and Analytics brings together five revised tutorial lectures from the 9th European Business Intelligence and Big Data Summer School (eBISS 2019). Rather than a single-author textbook, this is a carefully edited proceedings volume that captures a focused snapshot of current research and practice across several fast-moving areas.

The book’s strength lies in its range. One chapter looks at actionable conformance checking and process mining; another introduces text analytics and its pipeline of methods; a third surveys automated machine learning; a fourth addresses travel-time computation from GPS data; and the final chapter presents the Laplacian matrix as a tool for dimensionality reduction, visualization, and clustering. Together, the chapters show how modern data work often connects process analysis, textual information, mobility data, and machine learning techniques.

Why this volume stands out

Because the material comes from tutorial lectures, the tone is practical as well as research-oriented. The editors frame the collection for readers who want both conceptual grounding and an entry point into current methods. That makes it useful for graduate students, researchers, and technically minded practitioners who need a compact overview of where important questions and techniques are heading.

Subjects covered

• Business intelligence and big data analytics
• Process mining and conformance checking
• Text analytics workflows and applications
• Automated machine learning
• Mobility data and GPS-based travel-time analysis
• Graph-based methods for clustering and dimensionality reduction

A concise research-oriented collection

If you are looking for a broad but structured view of contemporary data analytics topics, this volume offers a solid scholarly starting point. It is especially well suited to readers who appreciate edited collections that balance overview, methods, and research context.