Abstract
The automated creation of cartographic products made with the software tools of geographic information systems (GIS) allow people to solve extensive and complicated tasks. These tasks lead to massive calculations which are very demanding in terms of data preparation, machine time and the memory capacity of hardware. This chapter deals with theoretic analysis of the complexity of tasks and the possibility of optimisation of sub-processes which lead to acceptable solutions. The quality of results achieved is also discussed here. Theoretical assumptions were verified through a data analysis project involving the storage of gas facilities under certain types of surface terrain in the Czech Republic. This analysis was performed in order to determine the re-built costs of gas facilities (pipelines) and the valuation of necessary costs in building new networks. The authors undertook this project for the GasNet, Ltd. Company which is a part of a RWE group in the Czech Republic. The results have general significance for the creation of cartographic products with GIS support.
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References
Bartoněk D, Bureš J (2012) Expert GIS for locating of archaeological sites, 4th International Conference on Cartography & GIS Albena, Bulgaria, June 2012, ISSN 1314-0604
Bartoněk D, Opatřilová I (2014) Design of man–machine interface for mobile mapping. Advanced Science Letters, Volume 20, Number 2, pp. 501-504 (4)
Bureš J, Bartoněk D, Opatřilová I (2013) Data analysis of surfaces above RWE gas pipelines in the Czech Republic. Project AdMaS ED2.1.00/03.0097 Nr. HS12357021212200. Brno University of Technology, Czech Republic (in Czech)
Cheby J, Wang W, Gao R (2012) Massive RDF data complicated query optimization based on MapReduce. Physics Procedia, Volume 25, pp. 1414-1419
Chetyrkin K - G, Kühn J - K, Sturm C (2007) Recent progress in computing four-loop massive correlators. Nuclear Physics B - Proceedings Supplements, Volume 164, pp. 203-206
Faro A, Giordano D, Majorána F (2011) Mining massive datasets by an unsupervised parallel clustering on a GRID: Novel algorithms and case study. Future Generation Computer Systems, Volume 27, Issue 6, pp. 711-724
Forslund D - W, Hansen C, Junker P, John W - S, Tenbrink S, Breton J. (1992) High-speed networks, visualization, and massive parallelism in the Advanced Computing Laboratory. Computing Systems in Engineering, Volume 3, Issues 1–4, pp. 521-524
Frache S, Chiabrando D, Graziano M, Vacca M, Boarino L, Zamboni M (2013) Enabling design and simulation of massive parallel nanoarchitectures. Journal of Parallel and Distributed Computing. Vol. 74, Issue 6, pp. 2530–2541
Juany B (2011) A Short Note on Data-Intensive Geospatial Computing. in Information Fusion and Geographic Information Systems. Lecture Notes in Geoinformation and Cartography 2011, pp 13-17
Juany L, Tang G, Liu X, Song X., Yang J, Liu K (2013) Parallel contributing area calculation with granularity control on massive grid terrain datasets. Computers & Geosciences, Volume 60, pp. 70-80
Luo Y – W, Wang X – L, Xu Z - Q (2005) An agent approach to spatial information grid architecture design. Computing and Informatics, Volume: 24, Issue: 2, pp. 201-222
Min J, -K; Park H, - H, Chung C - W (2005) Multi-way spatial join selectivity for the ring join graph: Information and Software Technology, Volume: 47, Issue: 12, pp. 785-795, DOI: 10.1016/j.infsof.2005.01.002
Park H - H, Min J - K, Chung, C - W, Chány T - G (2004) Multi-way R-tree joins using indirect predicates. Information and Software Technology, Volume 46, Issue 11, pp 739-751
Richter R, Döllner J (2013) Concepts and techniques for integration, analysis and visualization of massive 3D point clouds. Computers, Environment and Urban Systems, Volume 45, pp. 114–124
Sakellari G, Loukas G (2013) A survey of mathematical models, simulation approaches and testbeds used for research in cloud computing. Simulation Modelling Practice and Theory, Volume 39, pp. 92-103
Steinfadt S (2013) Fine-grained parallel implementations for SWAMP+ Smith–Waterman alignment. Parallel Computing, Volume 39, Issue 12, pp. 819-833
Sijbers J, den Dekker A – J, Scheunders P, Van Dyck D (1998) Maximum likelihood estimation of Rician distribution parameters. IEEE Transactions on Medical Imaging 17 (3): 357-361. doi:10.1109/42.712125. PMID 9735899
Xie Z, Wu L, Ye Z (2008) A Prototype Design of Parallelizing GIS operations. Conference: Geoinformatics 2008 and Joint Conference on GIS and Built Environment - Advanced Spatial Data Models and Analyses Location: Guangzhou, Republic of China, Proceedings of the SPIE, Volume 7146, pp. (2009), article id. 71462F, 9 pp
Yang C, Goodchild M, Qunying H (2011) Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing? International Journal of Digital Earth. Volume: 4 Issue: 4, pp. 305-329, Article Number PII 938806602 DOI: 10.1080/17538947.2011.587547
Yang D, Feng Y, Juan Y, Han X, Wang J, Li J (2013) Ad-hoc aggregate query processing algorithms based on bit-store for query intensive applications in cloud computing. Future Generation Computer Systems, Volume 29, Issue 7, pp. 1725-1735
Wang L, Tao J, Ranjan R, Marten H, Streit A., Chen J, Chen D (2013) G-Hadoop: MapReduce across distributed data centers for data-intensive computing. Future Generation Computer Systems, Volume 29, Issue 3, pp. 739-750
Weiss B, Bailey M (2011) Massive Parallel Computing to Accelerate Genome-Matching. GPU Computing, Gems NVIDIA Press, pp. 173-184, DOI: 10.1016/B978-0-12-384988-5.00012-7
Wen, J., Ma, Y., Liu, P., Sun, S. (2013). Distributed multipliers in MWM for analyzing job arrival processes in massive HPC workload datasets. Future Generation Computer Systems, ELSEVIER, http://dx.doi.org/10.1016/j.future.2013.12.009
Zhang W, Zhao M, Tu Z, Lou S (2011) Research and Implement of Distributed Nodes Collaboration-Based Management and Publishing Technologies for Massive Geospatial Information. Massive Computing. Advances in Intelligent and Soft Computing 2011 Vol. 112, pp. 391-400
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Bartoněk, D., Bureš, J., Opatřilová, I. (2014). The Solution of Massive Tasks in GIS Exemplified by Determining Terrain Surface Types Above Gas Pipelines in the Czech Republic. In: Bandrova, T., Konecny, M., Zlatanova, S. (eds) Thematic Cartography for the Society. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-08180-9_8
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