Today most applications continuously produce information under the form of\nstreams, due to the advent of the means of collecting data. Sensors and social\nnetworks collect an immense variety and volume of data, from different\nreal-life situations and at a considerable velocity. Increasingly, applications\nrequire processing of heterogeneous data streams from different sources\ntogether with large background knowledge. To use only the information on the\ndata stream is not enough for many use cases. Semantic Complex Event Processing\n(CEP) systems have evolved from the classical rule-based CEP systems, by\nintegrating high-level knowledge representation and RDF stream processing using\nboth the data stream and background static knowledge. Additionally, CEP\napproaches lack the capability to semantically interpret and analyze data,\nwhich Semantic CEP (SCEP) attempts to address. SCEP has several limitations;\none of them is related to their high processing time. This paper provides a\nconceptual model and an implementation of an infrastructure for distributed\nSCEP, where each SCEP operator can process part of the data and send it to\nother SCEP operators in order to achieves some answer. We show that by\nsplitting the RDF stream processing and the background knowledge using the\nconcept of SCEP operators, it's possible to considerably reduce processing\ntime.\n