While traditional database systems optimize for performance on one-shot queries, emerging large-scale monitoring applications require continuous tracking of complex aggregates and data-distribution summaries over collections of physically-distributed streams. Thus, effective solutions have to be simultaneously space efficient (at each remote site), communication efficient (across the underlying communication network), and provide continuous, guaranteed-quality estimates. In this paper, we propose novel algorithmic solutions for the problem of continuously tracking complex holistic aggregates in such a distributed-streams setting - our primary focus is on approximate quantile summaries, but our approach is more broadly applicable and can handle other holistic-aggregate functions (e.g., “heavy-hitters” queries). We present the first known distributed-tracking schemes for maintaining accurate quantile estimates with provable approximation guarantees, while simultaneously optimizing the storage space at each remote site as well as the communication cost across the network. In a nutshell, our algorithms employ a combination of local tracking at remote sites and simple prediction models for local site behavior in order to produce highly communication- and space-efficient solutions. We perform extensive experiments with real and synthetic data to explore the various tradeoffs and understand the role of prediction models in our schemes. The results clearly validate our approach, revealing significant savings over naive solutions as well as our analytical worst-case guarantees.
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