Towards a distributed, scalable and real-time RDF Stream Processing engine

Due to the growing need to timely process and derive valuable information and knowledge from data produced in the Semantic Web, RDF stream processing (RSP) has emerged as an important research domain. Of course, modern RSP have to address the volume and velocity characteristics encountered in the Big Data era. This comes at the price of designing high throughput, low latency, fault tolerant, highly available and scalable engines. The cost of implementing such systems from scratch is very high and usually one prefers to program components on top of a framework that possesses these properties, e.g., Apache Hadoop or Apache Spark. The research conducting in this PhD adopts this approach and aims to create a production-ready RSP engine which will be based on domain standards, e.g., Apache Kafka and Spark Streaming. In a nutshell, the engine aims to i) address basic event modeling to guarantee the completeness of input data in window operators, ii) process real-time RDF stream in a distributed manner efficient RDF stream handling is required; iii) support and extend common continuous SPARQL syntax easy-to-use, adapt to the industrial needs and iv) support reasoning services at both the data preparation and query processing levels.