Stream processing is becoming an omnipresent component in feature-rich computerized applications. The RDF data model is now frequently used to represent streams due to its data integration capabilities and support for reasoning services. In such situations, continuous extensions of the SPARQL query language are used to retrieve information from input streams. To efficiently process such queries, we claim that a representation aware of the regularity of incoming stream patterns is needed. In this paper, we present such a data format together with a dedicated query language which is equipped with inference features. Moreover, we highlight that queries in this language can be generated from machine learning-based processing of data streams. We emphasize the efficiency of our solution through an evaluation of real-world and synthetic datasets.

International audience

Real-time processing of data streams emanating from sensors is becoming a common task in industrial scenarios. An increasing number of processing jobs executed over such platforms are requiring reasoning mechanisms. The key implementation goal is thus to efficiently handle massive incoming data streams and support reasoning, data analytic services. Moreover, in an on-going industrial project on anomaly detection in large potable water networks, we are facing the effect of dynamically changing data and work characteristics in stream processing. The Strider system addresses these research and implementation challenges by considering scalability, fault-tolerance, high throughput and acceptable latency properties. We will demonstrate the benefits of Strider on an Internet of Things-based real world and industrial setting.