Least possible time paths in stochastic time-varying networks
Computers and Operations Research
Time-Expanded Graphs for Flow-Dependent Transit Times
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Capacity constrained routing algorithms for evacuation planning: a summary of results
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Time-Aggregated Graphs for Modeling Spatio-temporal Networks
Journal on Data Semantics XI
Spatio-Temporal Sensor Graphs (STSG): A data model for the discovery of spatio-temporal patterns
Intelligent Data Analysis - Knowledge Discovery from Data Streams
Modeling spatio-temporal network computations: a summary of results
GeoS'07 Proceedings of the 2nd international conference on GeoSpatial semantics
Spatio-temporal network databases and routing algorithms: a summary of results
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Characterizing sensor datasets with multi-granular spatio-temporal intervals
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Spatiotemporal neighborhood discovery for sensor data
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
Exploring multivariate spatio-temporal change in climate data using image analysis techniques
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
A Repository for Multirelational Dynamic Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
A modelling framework for social media monitoring
International Journal of Web Engineering and Technology
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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Given applications such as location based services and the spatio-temporal queries they may pose on a spatial network (eg. road networks), the goal is to develop a simple and expressive model that honors the time dependence of the road network. The model must support the design of efficient algorithms for computing the frequent queries on the network. This problem is challenging due to potentially conflicting requirements of model simplicity and support for efficient algorithms. Time expanded networks which have been used to model dynamic networks employ replication of the network across time instants, resulting in high storage overhead and algorithms that are computationally expensive. In contrast, the proposed time-aggregated graphs do not replicate nodes and edges across time; rather they allow the properties of edges and nodes to be modeled as a time series. Since the model does not replicate the entire graph for every instant of time, it uses less memory and the algorithms for common operations (e.g. connectivity, shortest path) are computationally more efficient than the time expanded networks.