Skip to main content

Formalization of Converting Processes and it Validation in Spatial Data Infrastructure

  • Conference paper
  • First Online:
Smart Technologies in Urban Engineering (STUE 2022)

Abstract

For transition to European standards for infrastructure development and spatial data (SD) bank supply following the Directive of the European Parliament and of the Council of Europe (INSPIRE), which is mandatory for all member states and candidates to join the EU, it is necessary to ensure uniform requirements for the content of electronic documents on individual spatial objects. Significant archives of information require automation of the converting process for differentiated databases of SD to updated rules of digitally describing all instances of SD. The main processes of data conversion are considered, based on which the formation technology of acceptable or missed information for spatial data infrastructure is constructed. The paper examines that affine transformation is recommended for cases where geometric distortions of SD are heterogeneous. It has been found that the process of converting spatial and attributive information is more complicated. It is shown that the converting of archival information is realized through a set of functional rules following a set of regulated rules (SRR). It is noted that conversion involves the process of bringing disparate SD in line with the new standards and classifications of SD. The processes of transforming disparate SD between coordinate systems and converting existing data sets relative to old and new classifiers have been formalized. It is established that verification and validation tools allow for detecting disaccord and forming the basis for further data ordering.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Baumann, P., Escriu, J.: INSPIRE coverages: an analysis and some suggestions. Open Geospatial Data, Software and Standards 4(1), 1–22 (2019). https://doi.org/10.1186/s40965-019-0059-x

    Article  Google Scholar 

  2. Feng, M.: Geodata for everyone - model-driven development and an example of INSPIRE WFS service. Open Geospatial Data, Software and Standards 1(1), 1–8 (2016). https://doi.org/10.1186/s40965-016-0007-y

    Article  Google Scholar 

  3. Ali, A., Emran, N.A., Asmai, S.A.: Missing values compensation in duplicates detection using hot deck method. Journal of Big Data 8(1), 1–19 (2021). https://doi.org/10.1186/s40537-021-00502-1

    Article  Google Scholar 

  4. Abdolmajidi, E., Harrie, L., Mansourian, A.: The stock-flow model of spatial data infrastructure development refined by fuzzy logic. Springerplus 5(1), 1–20 (2016). https://doi.org/10.1186/s40064-016-1922-1

    Article  Google Scholar 

  5. Karabegovic, A., Ponjavic, M., Ferhatbegovic, E., Karabegovic, E.: Spatial data and processes integration in local governance of Bosnia and Herzegovina. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1298–1303. IEEE, Opatija (2018). https://doi.org/10.23919/MIPRO.2018.8400235

  6. Idrizi, B.: General conditions of spatial data infrastructure. Int. J. Natu. Eng. Sci. 12(1), 57–62 (2018)

    Google Scholar 

  7. Zarytskyi, O.V., Kostenko, O.B., Bulaienko, M.V.: Automation of geospatial objects converting into the classifiers according to the European data standards. Mathematical Modeling and Computing 7(2), 228–238 (2020). https://doi.org/10.23939/mmc2020.02.228

  8. Ouanouki, R., April, A., Abran, A., Gomez, A., Desharnais, J.M.: Toward building RDB to HBase conversion rules. Journal of Big Data 4(1), 1–21 (2017). https://doi.org/10.1186/s40537-017-0071-x

    Article  Google Scholar 

  9. Zarytskyi, O., Bulaienko, M.: Development of algorithms for the geospatial array visualization module data in xml format. Municipal Economy of Cities 6(166), 8–14 (2021). https://doi.org/10.33042/2522-1809-2021-6-166-8-14 [in Ukrainian]

  10. Rizk, R., McKeever, S., Petrini, J., Zeitler, E.: Diftong: a tool for validating big data workflows. Journal of Big Data 6(1), 1–27 (2019). https://doi.org/10.1186/s40537-019-0204-5

    Article  Google Scholar 

  11. Klein, F.: Affine transformations. In: Elementary Mathematics from a Higher Standpoint, pp. 83–100. Springer, Berlin, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49445-5_7

  12. Kucher, O.V., Kurylyak, I.S., Staroverov, V.S., Koshelyuk, N.I.: Study of the method transforming existing geodesic, topographic-cartographic and cadastre materials to the USK-2000 coordinate system. Engineering Geodesy 64, 28–44 (2017). https://repositary.knuba.edu.ua/handle/987654321/2363 [in Ukrainian]

  13. Chub, I.A., Novozhylova, M.V.: Determination of descent direction in linearized problem of non-oriented geometric objects arrangement. Radio Electronics, Computer Science, Control 2, 263–270 (2010). https://doi.org/10.15588/1607-3274-2010-2-17

  14. Markič, Š., Donaubauer, A., Borrmann, A.: Enabling geodetic coordinate reference systems in building information modeling for infrastructure. In: Proceeding of the 17th International Conference on Computing in Civil and Building Engineering, pp. 5–7. Tampere, Finland (2018)

    Google Scholar 

  15. Nicolau, R., et al.: Harmonization of categorical maps by alignment processes and thematic consistency analysis. AIMS Geosciences 6(4), 473–490 (2020). https://doi.org/10.3934/geosci.2020026

    Article  Google Scholar 

  16. Baumann, P., Misev, D., Merticariu, V., Huu, B.P.: Array databases: concepts, standards, implementations. Journal of Big Data 8(1), 1–61 (2021). https://doi.org/10.1186/s40537-020-00399-2

    Article  Google Scholar 

  17. Zarytskyi, O.V., Kostenko, O.B., Bulaienko, M.V, Manakov, V.P.: Marking of incomplete spatially distributed information using validation. Bionics of Intelligence 1(94), 100–106 (2020). https://doi.org/10.30837/bi.2020.1(94).15 [in Ukrainian]

  18. Abba, A.H., Hassan, M.: Design and implementation of a CSV validation system. In: Proceedings of the 3rd International Conference on Applications in Information Technology (ICAIT'2018), pp. 111–116. ACM, New York (2018). https://doi.org/10.1145/3274856.3274879

  19. Maalem, S., Zarour, N.: Challenge of validation in requirements engineering. Journal of Innovation in Digital Ecosystems 3(1), 15–21 (2016). https://doi.org/10.1016/j.jides.2016.05.001

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Oleksandr Zarytskyi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zarytskyi, O., Kostenko, O., Bulaienko, M. (2023). Formalization of Converting Processes and it Validation in Spatial Data Infrastructure. In: Arsenyeva, O., Romanova, T., Sukhonos, M., Tsegelnyk, Y. (eds) Smart Technologies in Urban Engineering. STUE 2022. Lecture Notes in Networks and Systems, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-031-20141-7_1

Download citation

Publish with us

Policies and ethics