Article

Article title AN EVOLUTIONARY APPROACH TO THE USE OF SPATIAL DATA IN GEOGRAPHIC INFORMATION SERVICES
Authors S.L. Belyakov, A.V. Bozhenyuk, I.N. Rozenberg
Section SECTION I. EVOLUTIONARY MODELLING, GENETIC AND BIONIC ALGORITHMS
Month, Year 06, 2015 @en
Index UDC 519.688
DOI
Abstract The problems of adaptation of geographic information service to increase volume and modification of the spatial database structure are analyzed in this paper. The need to consider the factors changes the informational basis is generated by a visual way of searching and analysis of spatial data users of geographic information service. Traditionally, the decision of any applied problem begins with the construction of the workspace map. The final result of the analysis is determined by the content built workspace. Modern geoinformation systems and services include systems with big data, so the preparation of the original workspace has a complexity that exceeds the complexity of solving applied tasks. Without special measures the quality of the solution becomes low. This paper proposes a solution based on the principle of evolution of technical systems. In this problem, the evolutionary principle is the continuous generation of geoinformation service rules-productions that contains the knowledge of useful for visual analysis of cartographic objects. Rules are considered as hypotheses that require collective support to the clients of the service. Confirmation of any rule represents a selective sampling is useful for further use of knowledge. Thus, the proposed mechanism provides a continuous adaptation to the changing information environment due to the production and selection rules-productions. This paper analyzes the mechanism of generation and determined the structure of the rules-productions. The generation of rules by monitoring user activity, aggregation, generalization and transposition of existing rule sets is considered here. The mechanism of collective acknowledgement of the rules is exam- ined. We propose a new mechanism that uses a network of confirmation, which is based on a common spatial, temporal and semantic objects, classes, and relations within the rules.

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Keywords Visualization of spatial data; geographic information systems; intelligent systems; adaptation.
References 1. Rozenberg I.N. Sputnikovye i geoinformatsionnye tekhnologii v intellektual'nykh sistemakh upravleniya [Satellite and GIS technologies in intelligent control systems], Zheleznodorozhnyy transport [Railway Transport], 2013, No. 3, pp. 28-32.
2. G. Randy Keller, Chaitanya Baru. Geoinformatics: Cyberinfrastructure for the Solid Earth Sciences. Cambridge University Press, 2011.
3. Krishna Sinha Geoinformatics: Data to Knowledge. – Geological Society of America, 2006. 4. Allan Brimicombe, Chao Li Location-Based Services and Geo-Information Engineering. John Wiley & Sons, 2009.
5. Chaowei Yang, Min Sun, Kai Liu, Qunying Huang, Zhenlong Li, Zhipeng Gui, Yunfeng Jiang, Jizhe Xia, Manzhu Yu, Chen Xu, Peter Lostritto, Nanying Zhou Contemporary Computing Technologies for Processing Big Spatiotemporal Data, Space-Time Integration in Geography and GIScience, 2015, pp. 327-351.
6. Belyakov S.L., Belyakova M.L., Samoylov D.S. Geoinformatsionnyy servis situatsionnogo tsentra [Geoinformation Service of the Situational Center], Informatsionnye tekhnologii [Information Technologies], 2011, No. 8, pp. 29-32.
7. Berlyant A.M. Kartograficheskiy metod issledovaniya [The cartographic method of research]. Moscow: Izd-vo MGU, 1978, 255 p.
8. Holger Faby, Andreas Koch From maps to neo-cartography. Available at: http://cartographygis.com/pdf/64_Faby_Koch_Austria_paper.pdf (Accessed 07 June 2015)
9. Pettit C., Cartwright W., Bishop I., Lowell K., Puller D., Duncan D. Landscape Analysis and Visualisation, Spatial Models for Natural Resource Management and Planning. Berlin: Springer-Verlag, 2008.
10. Jacek Malczewski GIS-based land-use suitability analysis: a critical overview, Progress in Planning, 2004, Vol. 62, pp. 3-65.
11. Keim D.A. Information visualization and visual data mining, IEEE Transactions on Visualization and Computer Graphics, 2002, Vol. 8, pp. 1-8.
12. Li W., Linna L., Goodchild M.F., Anselin L. A geospatial cyberinfrastructure for urban economic analysis and spatial decision-making, ISPRS International Journal of Geo-Information, 2012, No. 2, pp. 413-431.
13. Wang, S A cyberGIS framework for the synthesis of cyberinfrastructure, GIS, and spatial analysis, Annals of the Association of American Geographers, 2010, Vol. 100, pp. 535-557.
14. Hart G., Dolbear C. Linked Data: A Geographic Perspective. Taylor & Francis, 2013.
15. Ahmed Loai Ali, Falko Schmid Data Quality Assurance for Volunteered Geographic Information, Geographic Information Science Lecture Notes in Computer Science, 2014, Vol. 8728, pp. 126-141.
16. Michael F. Goodchild, Linna Li Assuring the quality of volunteered geographic information, Spatial Statistics, 2012, Vol. 1, pp. 110-120.
17. David Fairbairn Mapping Disorder: An Exploratory Study. Modern Trends in Cartography, Lecture Notes in Geoinformation and Cartography, 2015, pp. 13-22.
18. Popovich, V., Vanurin, S., Kokh, S., Kuzyonny, V. Intellectual Geographic Information System for navigation safety, IEEE Aerospace and Electronic Systems Magazine, 2011, Vol. 26, pp. 29-31.
19. Edwin Lughofer Evolving Fuzzy Systems – Methodologies, Advanced Concepts and Applications. Springer Science & Business Media, 2011.
20. Plamen Angelov, Dimitar P. Filev,Nik Kasabov Evolving Intelligent Systems: Methodology and Applications. John Wiley & Sons, 2010.
21. Plamen Angelov Autonomous Learning Systems: From Data Streams to Knowledge in Realtime. John Wiley & Sons, 2012.
22. Available at: https://support.google.com/mapmaker/?hl=ru#topic=3180752 (Accessed 01 September 2014).
23. Available at: http://clubs.ya.ru/narod-karta/ (Accessed 01 September 2014).
24. Belyakov S.L., Rozenberg I.N. Programmnye intellektual'nye obolochki geoinformatsionnykh system [Software intellectual sheath geographic information systems]. Moscow: Nauchnyy mir, 2010, 132 p.
25. Belyakov S.L., Didenko D.A., Samoylov D.S. Adaptivnaya protsedura upravleniya predstavleniem rabochey oblasti elektronnoy karty [Adaptive procedure of management by representation of working area of an electronic card], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2011, No. 1 (114), pp. 125-130.
26. Luger G.F. Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Addison Wesley. 2004.
27. Vagin V.N., Golovina E.Yu., Zagoryanskaya A.A., Fomina M.V. Dostovernyy i pravdopodobnyy vyvod v intellektual'nykh sistemakh [Credible and plausible inference in intelligent systems], Under ed. Vagina V.N. i Pospelova D.A. Moscow: Fizmatlit, 2008, 712 c.
28. Stanislav L. Belyakov, Alexander V. Bozhenyuk, Marina L. Belykova, Igor N. Rozenberg. Model Of Intellectual Visualization Of Geoinformation Service, Proceedings 28th European Conference on Modelling and Simulation ECMS 2014, Flaminio Squazzoni, Fabio Baronio, Claudia Archetti, Marco Castellani (Editors), pp. 326-332.
29. Kormen T., Leyzerson Ch., Rivest R., Shtayn K. Algoritmy. Postroenie i analiz [Algorithms. The construction and analysis], 2nd ed. Moscow: Vil'yams, 2005, 1296 p.

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