Many of today’s big data applications are web applications, or they depend on data from the web. The Internet of Things (IoT), monitoring systems, some data mining and generative artificial intelligence (AI), for example, process data streams as they are produced to make decisions or evaluate hypotheses.

Web stream processing extends linked data (LD) by combining stream processing with semantic web methods. The peculiarities of web data streams, which are usually in high volumes, volatile, and generated at high velocity, still pose research challenges.

Streaming linked data (SLD) is the topic of this book, which collects in six chapters the initial challenges, methods, languages, and results reported in about 15 years of research. While for LD the methods and languages to represent and connect knowledge in the semantic web are assessed, and are somehow in common with data mining and AI, the extensions to stream reasoning are still under development, starting from the Resource Description Framework (RDF), the Web Ontology Language (OWL), and the query language SPARQL. As the authors observe, streaming data is characterized by the velocity dimension, the semantic web methods by the variety of structured data, and SLD covers both dimensions.

Using a tutorial style, the book introduces the challenges of stream reasoning when it comes to developing LD into SLD. Chapter 2 summarizes the background knowledge. Chapter 3 details RDF stream processing and the RSP-QL language family. Chapter 4 discusses the life cycle of SLD, and presents a possible standard model. Chapter 5 compares the experimental environments for RDF stream processing and the available benchmarks. The last chapter proposes practical exercises and their solutions. A simple example of a stream of colors is used throughout the book.

It is definitely a book for researchers and PhD students, as the reader should have some knowledge of the basic concepts of the semantic web and data mining, here recalled but not explained. Researchers of the semantic web can find easy access to the relevant literature, listed at the end of each chapter. However, researchers in data mining and AI should note that important issues about the integrity, privacy, and security of data are not considered.

I agree with the author of the book’s foreword: this is “an advanced textbook on a topic for which there was not such compiled material before.”