Recent years have witnessed the growth of a new class of data-intensive applications that do not fit the DBMS data model and querying paradigm. Instead, the data arrive at High speeds taking the form of an unbounded sequence of values (data streams) and queries run continuously returning new results as new data arrive. In these applications, data streams from external sources flow into a Data Stream Management System (DSMS) where they are processed by different operators. Many applications share the same need for processing data streams in a continuous fashion. For most distributed streaming applications, the centralized processing of continuous queries over distributed data is simply not viable. This research addresses the problem of computing continuous join queries over distributed data streams.We present a new method, called DHTJoin that exploits the power of a Distributed Hash Table (DHT) combining hash-based placement of tuples and dissemination of queries by exploiting the embedded trees in the underlying DHT, thereby incuring little overhead. Unlike state of the art solutions that index all data, DHTJoin identifies, using query predicates, a subset of tuples in order to index the data required by the user's queries, thus reducing network traffic. DHTJoin tackles the dynamic behavior of DHT networks during query execution and dissemination of queries. We provide a performance evaluation of DHTJoin which shows that it can achieve significant performance gains in terms of network traffic.
