Space-bounded Extreme Aggregation of Data Stream over Time-based Sliding Window

Data processing in Cloud or IoT (Internet of Things) sometimes implies continuous real-time queries as data streams. In order to acquire extreme value of data stream over time-based sliding window, traditional approaches computed the exact solution through vast space especially under worse circumstances. In this paper, we design space-bounded synopsis data structure and random algorithm for extreme aggregation to get non-exact solution by finite extrema candidates over time sliding window, whose validity can be theoretically guaranteed. Comprehensive experiments on synthetic and real data set are designed to analyze the tradeoff between accuracy and overhead, which also illustrate the effectiveness.