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Spatial Predictive Query Processing on Road Networks

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Encyclopedia of GIS

Synonyms

Destination prediction; iRoad system; Moving objects; Network mobility model; Predictive spatiotemporal queries; Predictive tree; P-tree; Reachability tree; Road networks; Shortest path tree

Definition

Predictive queries on road networks answer questions and inquiries that are based on the anticipated future locations of a set of moving objects traveling on road networks. A main difference between Euclidean space and road network space is objects in the former are free to move anywhere in the given space. However, in the latter, objects’ movements are constrained by the underlying road segments, intersections, and speed and capacity limits on each road. Also, in Euclidean space, the Euclidean distance is the official measure of distance between two different locations on the space.

On the side of road network space, the network distance (i.e., distance of the shortest path between two locations along a road network), is the measure. Fundamentally, predictive queries on road...

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References

  • Hendawi AM, Mokbel MF (2012a) Panda: a predictive spatio-temporal query processor. In: Proceedings of the ACM SIGSPATIAL international conference on advances in geographic information systems, ACM SIGSPATIAL GIS, Redondo Beach

    Book  Google Scholar 

  • Hendawi AM, Mokbel MF (2012b) Predictive spatio-temporal queries: a comprehensive survey and future directions. In: Proceeding of the ACM SIGSPATIAL GIS international workshop on mobile geographic information systems, MobiGIS, Redondo Beach, Nov 2012

    Google Scholar 

  • Hendawi AM, Bao J, Mokbel MF (2013) iRoad: a framework for scalable predictive query processing on road networks. In: Proceedings of the international conference on very large data bases, VLDB, Riva Del Garda

    Google Scholar 

  • Hendawi AM, Bao J, Mokbel MF, Ali M (2015) Predictive tree: an efficient index for predictive queries on road networks. In: Proceedings of the international conference on data engineering, ICDE, Seoul

    Google Scholar 

  • Jeffrey D, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51:107–113

    Google Scholar 

  • Jeung H, Liu Q, Shen HT, Zhou X (2008) A hybrid prediction model for moving objects. In: Proceedings of the international conference on data engineering, ICDE, Cancún, Apr 2008, pp 70–79

    Google Scholar 

  • Jeung H, Yiu ML, Zhou X, Jensen CS (2010) Path prediction and predictive range querying in road network databases. VLDB J 19(4):585–602

    Article  Google Scholar 

  • Krumm J (2006) Real time destination prediction based on efficient routes. In: Proceedings of the society of automotive engineers world congress, SAE, Michigan, Apr 2006

    Google Scholar 

  • Krumm J (2008) How people use their vehicles: statistics from the 2009 national household travel survey. In: Proceedings of the society of automotive engineers world congress, SAE, Michigan, Apr 2008

    Google Scholar 

  • Krumm J, Gruen R, Delling D (2011) From destiantion prediction to route prediction. Technical report. Microsoft research

    Google Scholar 

  • Li Y, George S, Apfelbeck C, Hendawi AM, Hazel D, Teredesai A, Ali M (2014) Routing service with real world severe weather. In: Proceedings of the ACM SIGSPATIAL international conference on advances in geographic information systems, ACM SIGSPATIAL GIS, Dallas, Nov 2014

    Google Scholar 

  • Tao Y, Faloutsos C, Papadias D, Liu B (2004) Prediction and indexing of moving objects with unknown motion patterns. In: Proceedings of the ACM international conference on management of data, SIGMOD, Paris, June 2004, pp 611–622

    Google Scholar 

  • Tao Y, Papadias D, Sun J (2003) The TPR*-tree: an optimized spatio-temporal access method for predictive queries. In: Proceedings of the international conference on very large data bases, VLDB, Berlin, Sept 2003, pp 790–801

    Google Scholar 

  • Zhang R, Jagadish HV, Dai BT, Ramamohanarao K (2010) Optimized algorithms for predictive range and KNN queries on moving objects. Inf Syst 35(8):911–932

    Article  Google Scholar 

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Correspondence to Abdeltawab M. Hendawi .

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Hendawi, A.M., Mokbel, M.F., Ali, M. (2017). Spatial Predictive Query Processing on Road Networks. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_1590

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