Workload-aware wavelet synopses for sliding window aggregates

In this work, we study the problem of maintaining basic aggregate statistics over a sliding-window data stream under the constraint of limited memory. As in IoT scenarios the available memory is typically much less than the window size, queries are answered from compact synopses that are maintained in an online fashion. For the efficient construction of such synopses, we propose wavelet-based algorithms that provide deterministic guarantees and produce near exact results for a variety of data distributions. Furthermore, we show how accuracy can be further improved when workload information is known. For this purpose, we propose a workload-aware streaming system that trade-offs accuracy with synopsis’ construction throughput. The conducted experiments indicate that with only a $$15\%$$ penalty in throughput, the proposed system produces fairly accurate results even for the most adversarial distributions.