Online Amnesic Summarization of Streaming Locations

Abstract

Massive data streams of positional updates become increasingly difficult to manage under limited memory resources, especially in terms of providing near real-time response to multiple continuous queries. In this paper, we consider online maintenance for spatiotemporal summaries of streaming positions in an aging-aware fashion, by gradually evicting older observations in favor of greater precision for the most recent portions of movement. Although several amnesic functions have been proposed for approximation of time series, we opt for a simple, yet quite efficient scheme that achieves contiguity along all retained stream pieces. To this end, we adapt an amnesic tree structure that effectively meets the requirements of time-decaying approximation while taking advantage of the succession inherent in positional updates. We further exemplify the significance of this scheme in two important cases: the first one refers to trajectory compression of individual objects; the other offers estimated aggregates of moving object locations across time. Both techniques are validated with comprehensive experiments, confirming their suitability in maintaining online concise synopses for moving objects.