Probabilistic counting algorithms for data base applications
Journal of Computer and System Sciences
Progressive approximate aggregate queries with a multi-resolution tree structure
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Temporal Aggregation over Data Streams Using Multiple Granularities
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Maintaining time-decaying stream aggregates
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Processing set expressions over continuous update streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Spatio-Temporal Aggregation Using Sketches
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Online Amnesic Approximation of Streaming Time Series
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Sampling Trajectory Streams with Spatiotemporal Criteria
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
Amnesic online synopses for moving objects
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Resource sharing in continuous sliding-window aggregates
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Multi-scale windowing over trajectory streams
ER'12 Proceedings of the 2012 international conference on Advances in Conceptual Modeling
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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.