Streams of data are now massively, constantly arrive from Internet of Things Devices (IoT), sensors and social media, and require rapid processing, querying and integration with background knowledge in order to support further data analysis. To this direction, RDF Stream processing platforms are valuable tools, enabling query answering over RDF streams. However, so far, the current state-of-the-art in RDF Stream processing has provided either centralized engines that cannot deal with massive RDF data streams or distributed engines that offer limited reasoning capabilities. Summarization techniques on the other hand have already proved their value for indexing data, query answering, reasoning, source selection, graph visualization, and schema discovery. However, to the best of our knowledge they have not yet been exploited for stream data, which remains a completely unexplored area. iQARuS objective is to enable effective and efficient query answering over RDF stream data using summaries. Generated summaries will be smaller than the original graphs and as such they will reduce drastically the data space, enabling efficient query answering and reasoning. However, due to this reduced data space exact answers might not be always possible to be retrieved directly from summaries. We intend to explore approximate query answering and then to offer exploration operations that will allow expanding the summaries for exact query answering. In addition, incremental algorithms will enable summary updating to avoid the overhead of summary recomputation from scratch. The developed solution will cover and combine both recent window stream data and background, staged knowledge and will be evaluated extensively using both well-established RDF Stream Processing benchmarks and a new one to be generated during the lifetime of the project.