Linked Stream Data has emerged as an effort to represent dynamic, time-dependent data streams following the principles of Linked Data. Given the increasing number of available stream data sources like sensors and social network services, Linked Stream Data allows an easy and seamless integration, not only among heterogenous stream data, but also between streams and Linked Data collections, enabling a new range of real-time applications. This tutorial gives an overview about Linked Stream Data processing. It describes the basic requirements for the processing, highlighting the challenges that are faced, such as managing the temporal aspects and memory overflow. It presents the different architectures for Linked Stream Data processing engines, their advantages and disadvantages. The tutorial also reviews the state of the art Linked Stream Data processing systems, and provide a comparison among them regarding the design choices and overall performance. A short discussion of the current challenges in open problems is given at the end.
