Continuous queries over nonterminating data streams usually specify windows in order to obtain an evolving –yet bounded– set of tuples and thus provide timely and incremental results. In this talk, we introduce a multi-level sliding window operator that concurrently spans overlapping time periods, essentially providing at each granularity a varying, but always finite portion of the most recent stream items. Taking advantage of its properties, we suggest a suitable data structure that can efficiently maintain tuples qualifying for each granular level. We propose techniques for evaluating advanced continuous requests against multiple time horizons, achieving near real-time response at reduced overhead. Moreover, this framework can also assist in the analysis of evolving trajectories generated by the streaming locations of moving point objects, like GPS-equipped vehicles, commodities with RFID’s, etc. Such spatiotemporal windows can actually abstract trajectories at progressively coarser resolutions towards the past, while retaining finer features closer to the present. This framework has been empirically validated against real and synthetic datasets, offering concrete evidence of its benefits to online stream processing.