Amnesic Online Synopses for Moving Objects Michalis Potamias, Kostas Patroumpas, and Timos Sellis

1. Amnesic Online Synopses for Moving ObjectsMichalis Potamias, Kostas Patroumpas, and Timos Sellis 4. Applications on Moving Objects 1. Overview We present an amnesictree structure for online maintenance of time-decaying synopses over streaming data. We exemplify such an behavior over streams of locations taken from numerous moving objects in order to obtain trajectory approximations as well as affordable estimates regarding distinct count spatiotemporal queries. • Compressing Single Trajectory • Store displacements between consecutive locations • multiple resolutions of a trajectory • tuple: < id, x, y, t > • Spatiotemporal Distinct Count • 3-tier Compression • x-y plane: Spatial Grid • time: AmTree • query: FMsketch • Updating • merge sketches (OR) • Answering Queries (α, ΔΤ): • Bounding β • Bounding ΔΤ’ • Example: • Query: ( α , [135..220] ) • Estim.: ( β , [128..223] ) • 2. AmTree • Each item contributes to the structure according to its age. • Older items only in coarser granularity levels. • Recent items in fine granularity levels. • Complexity: • Update per tuple: O(1) • Space: O(logN) 3. Updating example • 5. Future work • what about other amnesic patterns? • can we define similar structures? 6. Extension more information: log2 N, same update complexity: O(1) 7. References A. Bulut and A.K. Singh. SWAT: Hierarchical Stream Summarization inLarge Networks.ICDE, 2003. T. Palpanas, M. Vlachos, E. Keogh, D. Gunopulos, and W. Truppel. Online Amnesic Approximation of Streaming Time Series.ICDE, 2004. M. Potamias, K. Patroumpas, and T. Sellis. Online Amnesic Summarization of Streaming Locations. T.R., NTUA,2006. Y. Tao, G. Kollios, J. Considine, F. Li, and D.Papadias. Spatio-TemporalAggregation Using Sketches.ICDE, 2004.