Data stream management systems need to control their resources adaptively since stream characteristics as well as query workload vary over time. In this paper we investigate an approach to adaptive resource management for continuous sliding window queries that adjusts window sizes and time granularities to keep resource usage within bounds. These two novel techniques differ from standard load shedding approaches based on sampling as they ensure exact query answers for given user-defined Quality of Service specifications, even under query re-optimization. In order to quantify the effects of both techniques on the various operations in a query plan, we develop an appropriate cost model for estimating operator resource allocation in terms of memory usage and processing costs. A thorough experimental study not only validates the accuracy of our cost model but also demonstrates the efficacy and scalability of the proposed techniques.

In order to support continuous queries over data streams, a plethora of suitable techniques as well as prototypes have been developed and evaluated in recent years. In particular, it is of utmost importance to confirm their necessity and feasibility in real-world applications. For that reason, we have successfully coupled our infrastructure for data stream processing (PIPES) with an industrial Production-to-Business software (i-Plant) dedicated to highly automated manufacturing processes.

Adaptivity is a challenging open issue in data stream management. In this paper, we tackle the problem of memory adaptivity inside a system executing temporal sliding window queries over continuous data streams. Two different techniques to control the memory usage at runtime are proposed which refer to changes in window sizes and time granularities. Both techniques differ from standard load shedding approaches based on sampling as they ensure precise query answers for user-defined Quality of Service (QoS) specifications, even under query re-optimization.