Abstract Data streams which are now massively and constantly arriving from Internet of Things devices, sensors and social media, require efficient processing, querying and reasoning within a given timeframe. With this in mind, the RDF data model, the cornerstone of the Web of Data, supports a feature-rich stream processing ecosystem that takes into account the temporal dimension associated with events. These timestamped streams support advanced temporal analysis ranging from time-based queries, temporal anomaly detection to temporal reasoning. This survey is the first to provide a comprehensive overview of the field of RDF stream processing, focusing on (query) languages, systems, and benchmarks. For each of these areas, we present salient dimensions, propose a taxonomy of existing work, detail the concepts at the core of each approach and describe their main technical aspects and implementation. We hope that the survey will help readers understand this scientifically rich field and identify the most relevant method for various usage scenarios.

Data streams are becoming omnipresent on the Web. The Stream Reasoning (SR) paradigm, which combines Stream Processing with Semantic Web techniques, has been successful in processing these data streams. The progress in SR research has led to several applications in domains such as the Internet of Things , social media analysis, Smart Cities, and many others. Each of these applications produces and consumes data streams, however, there are no fixed guidelines on how to manage data streams on the Web, as there are for their static counterparts. More specifically, there is no fixed life cycle for Streaming Linked Data (SLD) yet. Tommasini et al. (2020) introduced an initial proposal for a SLD life cycle , however, it has not been verified if the proposed life cycle captures existing applications and no guidelines were given for each step. In this paper, we survey existing SR applications and identify if the life cycle proposed by Tommasini et al. fully captures the surveyed applications. Based on our analysis, we found that some of the steps needed reordering or being split up. This paper proposes an update of the life cycle and surveys the existing literature for each life cycle step while proposing a number of guidelines and best practices. Compared to the initial proposal by Tommasini et al., we drill down into the details of the processing step which was previously neglected. The updated life cycle and guidelines serves as a blueprint for future SR applications. A life cycle for SLD that allows to efficiently manage data streams on the web, brings us a step closer to the realization of the SR vision.