Today many applications routinely generate large quantities of data. The data often takes the form of (time) series, or more generally streams, i.e. an ordered se-quence of records. Analysis of this data requires stream processing techniques which differ in significant ways from what current database analysis and query tech-niques have been optimized for. In this paper we present a new operator, called StreamJoin, that can efficiently be used to solve stream-related problems of various appli-cations, such as universal quantification, pattern recog-nition and data mining. Contrary to other approaches, StreamJoin processing provides rapid response times, a non-blocking execution as well as economical resource utilization. Adaptability to different application scenarios is realized by means of parameters. In addition, the StreamJoin operator can be efficiently embedded into the database engine, thus implicitly using the optimization and parallelization capabilities for the benefit of the ap-plication. The paper focuses on the applicability of StreamJoin to integrate application semantics into the DBMS.