Massive data stream processing is a scientific challenge and an industrial concern. But with the current volumes of data streams , their number and variety, current techniques are not able to meet the requirements of applications. The Semantic Web tools , through the RDF for example, allow to address the problem of heterogeneous data. Thus, the data stream are converted to semantic data stream by using RDF triples extended with a timestamp. To be able to query , filter, or reason semantic data streams, the query language SPARQL must be extended to include concepts such as windowing , based on what has been done in Data Stream Management Systems. In this talk, I will present recent work on the semantic data stream management , particularly extensions made on SPARQL language and associated benchmarks.