Abstract
Given emerging diverse spatio temporal network (STN) datasets, e.g., GPS tracks, temporally detailed roadmaps and traffic signal data, the aim is to develop a logical data-model which achieves a seamless integration of these datasets for diverse use-cases (queries) and supports efficient algorithms. This problem is important for travel itinerary comparison and navigation applications. However, this is challenging due to the conflicting requirements of expressive power and computational efficiency as well as the need to support ever more diverse STN datasets, which now record non-decomposable properties of n-ary relations. Examples include travel-time and fuel-use during a journey on a route with a sequence of coordinated traffic signals and turn delays. Current data models for STN datasets are limited to representing properties of only binary relations, e.g., distance on individual road segments. In contrast, the proposed logical data-model, Lagrangian Xgraphs can express properties of both binary and n-ary relations. Our initial study shows that Lagrangian Xgraphs are more convenient for representing diverse STN datasets and comparing candidate travel itineraries.
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References
Shekhar, S., Gunturi, V., Evans, M.R., Yang, K.: Spatial big-data challenges intersecting mobility and cloud computing. In: MobiDE, pp. 1–6. ACM (2012)
Liu, H., Hu, H.: Smart-signal phase ii: Arterial offset optimization using archived high-resolution traffic signal data. Technical Report CTS 13-19, Intel. Trans. Sys. Inst., Center for Transportation Studies, Univ. of Minnesota (April 2013)
Yuan, J., et al.: T-drive: driving directions based on taxi trajectories. In: Proc. of the SIGSPATIAL, pp. 99–108. ACM (2010)
Manyika, J., et al.: Big data: The next frontier for innovation, competition and productivity. McKinsey Global Institute (May 2011), http://goo.gl/AA8DS
Lovell, J.: Left-hand-turn elimination. NY Times (December 9, 2007)
Demiryurek, U., Banaei-Kashani, F., Shahabi, C.: A case for time-dependent shortest path computation in spatial networks. In: Proc. SIGSPATIAL. ACM (2010)
Koonce, P., et al.: Traffic signal timing manual. Technical Report FHWA-HOP-08-024, US Dept of Trans. Federal Higway Admin. (June 2008)
George, B., Shekhar, S.: Time-aggregated graphs for modelling spatio-temporal networks. J. Semantics of Data XI 191 (2007)
Köhler, E., Langkau, K., Skutella, M.: Time-expanded graphs for flow-dependent transit times. In: Möhring, R.H., Raman, R. (eds.) ESA 2002. LNCS, vol. 2461, pp. 599–611. Springer, Heidelberg (2002)
Hoel, E.G., Heng, W.-L., Honeycutt, D.: High performance multimodal networks. In: Medeiros, C.B., Egenhofer, M., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 308–327. Springer, Heidelberg (2005)
Güting, R.H.: Graphdb: Modeling and querying graphs in databases. In: Proc. of the 20th International Conference on Very Large Data Bases, pp. 297–308 (1994)
Zheng, Y., Zhou, X.E. (eds.): Computing with Spatial Trajectories. Springer (2011)
Shashi: Spatial pictogram enhanced conceptual data models and their translation to logical data models. In: Intl. Works. on Integrated Spatial Databases, Digital Inages and GIS, pp. 77–104. Springer, Heidelberg (1999)
Bedard, Y.: Visual modeling of spatial databases: Towards spatial PVL and UML. Geoinformatica 53(2) (1999)
Shekhar, S., et al.: Data models in geographic information systems. Commun. ACM 40(4) (April 1997)
Batchelor, G.: An introduction to fluid dynamics. Cambridge Univ. Press (1973)
Gunturi, V.M.V., Nunes, E., Yang, K., Shekhar, S.: A critical-time-point approach to all-start-time lagrangian shortest paths: A summary of results. In: Pfoser, D., Tao, Y., Mouratidis, K., Nascimento, M.A., Mokbel, M., Shekhar, S., Huang, Y. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 74–91. Springer, Heidelberg (2011)
Parent, C., et al.: Spatio-temporal conceptual models: Data structures + space + time. In: Proc. of the Intl. Symp. on Adv. in GIS, pp. 26–33. ACM (1999)
Tøssebro, E., Nygård, M.: Representing topological relationships for spatiotemporal objects. GeoInformatica 15(4), 633–661 (2011)
Fileto, R., Krüger, M., Pelekis, N., Theodoridis, Y., Renso, C.: Baquara: A holistic ontological framework for movement analysis using linked data. In: Ng, W., Storey, V.C., Trujillo, J.C. (eds.) ER 2013. LNCS, vol. 8217, pp. 342–355. Springer, Heidelberg (2013)
Gallo, G., Longo, G., Pallottino, S., Nguyen, S.: Directed hypergraphs and applications. Elsevier, Discrete applied mathematics 42(2), 177–201 (1993)
Berge, C.: Graphs and Hypergraphs. Elsevier Science Ltd (1985)
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Gunturi, V.M.V., Shekhar, S. (2014). Lagrangian Xgraphs: A Logical Data-Model for Spatio-Temporal Network Data: A Summary. In: Indulska, M., Purao, S. (eds) Advances in Conceptual Modeling. ER 2014. Lecture Notes in Computer Science, vol 8823. Springer, Cham. https://doi.org/10.1007/978-3-319-12256-4_21
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DOI: https://doi.org/10.1007/978-3-319-12256-4_21
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