amnesic online synopses for moving objects michalis potamias, kostas patroumpas, and timos sellis

33. Updating example. Updating example

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

11. Overview. OverviewWe present an amnesic tree 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.

22. AmTree. AmTree

44. Applications on . Applications on Moving ObjectsMoving Objects

66. Extension. Extensionmore information: log2 N, same update complexity: O(1)

•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)

77. References. ReferencesA. Bulut and A.K. Singh. SWAT:

Hierarchical Stream Summarization in Large 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-Temporal Aggregation Using Sketches. ICDE, 2004.

1. Compressing Single Trajectory• Store displacements between

consecutive locations• multiple resolutions of a

trajectory

tuple: < id, x, y, t >

2. 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] )

55. Future work. Future work• what about other amnesic patterns?

• can we define similar structures?