A data model and data structures for moving objects databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A foundation for representing and querying moving objects
ACM Transactions on Database Systems (TODS)
The 8 requirements of real-time stream processing
ACM SIGMOD Record
Spatio-temporal data reduction with deterministic error bounds
The VLDB Journal — The International Journal on Very Large Data Bases
Online amnesic summarization of streaming locations
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Multi-granular spatio-temporal object models: concepts and research directions
ICOODB'09 Proceedings of the Second international conference on Object databases
Multi-granular Time-Based Sliding Windows over Data Streams
TIME '10 Proceedings of the 2010 17th International Symposium on Temporal Representation and Reasoning
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Many modern monitoring applications collect massive volumes of positional information and must readily respond to a variety of continuous queries in real-time. An important class of such requests concerns evolving trajectories generated by the streaming locations of moving point objects, like GPS-equipped vehicles, commodities with RFID's etc. In this paper, we suggest an advanced windowing construct that enables online, incremental examination of recent motion paths at multiple levels of detail. This spatiotemporal operator can actually abstract trajectories at progressively coarser resolutions towards the past, while retaining finer features closer to the present. We explain the semantics of such multi-scale sliding windows through parametrized functions that can effectively capture their spatiotemporal properties. We point out that window specification is much more than a powerful means for efficient processing of multiple concurrent queries; it can be also used to obtain concrete subsequences from each trajectory, thus preserving continuity in time and contiguity in space for the respective segments. Finally, we exemplify window utilization for characteristic queries and we also discuss algorithmic challenges in their ongoing implementation.