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Analytics provided on top of large scale data streams are the key research subject for future decision making applications. The huge volumes of data make their partitioning imperative to efficiently support novel applications. Such applications should be based on intelligent, efficient methods for querying multiple data partitions. A processor is placed in front of each partition dedicated to manage/execute queries for the specific piece of data. Continuous queries over these data sources require intelligent mechanisms to result the final outcome (query response) in the minimum time with the maximum performance. This paper proposes a mechanism for handling the behavior of an entity that undertakes the responsibility of handling the incoming queries. Our mechanism adopts a time-optimized scheme for selecting the appropriate processor(s) for each incoming query through the use of the Odds algorithm. We …