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ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
Explicit Graphs in a Functional Model for Spatial Databases
IEEE Transactions on Knowledge and Data Engineering
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IEEE Transactions on Knowledge and Data Engineering
Temporal Entity-Relationship Models-A Survey
IEEE Transactions on Knowledge and Data Engineering
Time-Expanded Graphs for Flow-Dependent Transit Times
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Spatio-Temporal Databases
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VLDB '05 Proceedings of the 31st international conference on Very large data bases
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SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
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CoMoGIS'06 Proceedings of the 2006 international conference on Advances in Conceptual Modeling: theory and practice
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Spatio-temporal network is defined by a set of nodes, and a set of edges, where the properties of nodes and edges may vary over time. Such networks are encountered in a variety of domains ranging from transportation science to sensor data analysis. Given a spatio-temporal network, the aim is to develop a model that is simple, expressive and storage efficient. The model must also provide support for the design of algorithms to process frequent queries that need to be answered in the application domains. 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. This model is generally used to represent time-dependent flow networks and tends to be application-specific in nature. 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. Our approach achieves physical data independence and also addresses the issue of modeling spatio-temporal networks that do not involve flow parameters. In this paper, we describe the model at the conceptual, logical and physical levels. We also present case studies from various application domains.