In this paper, a novel approach for building synopses is proposed by using a service and message-oriented architecture. The \SEQ summarization system initially designed for very large stored databases, by its intrinsic features, is capable of dealing with the requirements inherent to the data stream environment. Its incremental maintenance of the output summaries and its scalability allows it to be a serious challenger to existing techniques. The resulting summaries present on the one hand the incoming data in a less precise form but is still on the other hand very informative on the actual content. We expose a novel way of exploiting this semantically rich information for query answering with an approach mid-way between blunt query answering and mid-way between data mining.