ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume IV-4/W5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W5, 65–72, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W5-65-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W5, 65–72, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W5-65-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Oct 2017

23 Oct 2017

TOPOLOGICALLY CONSISTENT MODELS FOR EFFICIENT BIG GEO-SPATIO-TEMPORAL DATA DISTRIBUTION

M. W. Jahn1, P. E. Bradley2, M. Al Doori3, and M. Breunig1 M. W. Jahn et al.
  • 1KIT - Karlsruhe Institute of Technology, Geodetic Institute, Germany
  • 2KIT - Karlsruhe Institute of Technology, Institute of Photogrammetry and Remote Sensing, Germany
  • 3AUD - American University of Dubai, Department of Electrical and Computer Engineering, United Arab Emirates

Keywords: big geo-spatial data, big geo-spatio-temporal data, nd-databases, nd-topology models, topological consistency, parallel databases, parallel geo-analytics and -simulations

Abstract. Geo-spatio-temporal topology models are likely to become a key concept to check the consistency of 3D (spatial space) and 4D (spatial + temporal space) models for emerging GIS applications such as subsurface reservoir modelling or the simulation of energy and water supply of mega or smart cities. Furthermore, the data management for complex models consisting of big geo-spatial data is a challenge for GIS and geo-database research. General challenges, concepts, and techniques of big geo-spatial data management are presented. In this paper we introduce a sound mathematical approach for a topologically consistent geo-spatio-temporal model based on the concept of the incidence graph. We redesign DB4GeO, our service-based geo-spatio-temporal database architecture, on the way to the parallel management of massive geo-spatial data. Approaches for a new geo-spatio-temporal and object model of DB4GeO meeting the requirements of big geo-spatial data are discussed in detail. Finally, a conclusion and outlook on our future research are given on the way to support the processing of geo-analytics and -simulations in a parallel and distributed system environment.