Article

Article title ADAPTATION PROCEDURE OF THE SPATIAL DATA VISUALIZATION MADE BY GEOINFORMATION SERVICES
Authors S.L. Belyakov, A.V. Bozhenyuk, I.N. Rozenberg
Section SECTION IV. INFORMATION AND COMMUNICATION TECHNOLOGIES
Month, Year 03, 2015 @en
Index UDC 519.688
DOI
Abstract The article deals with the task of analyzing the adaptation procedure of geoinformation service with contensive filling of its own database and associated with changes of the behavior of users that visually analyze spatial data. This problem relates to the fundamental problem of minimizing redundant data flows in networks. The relevance of the research of process visualization is dictated by the fact that the geo-information services operate "big data." This means that obtaining data for applied problem requires a significant investment of resources and has a significant impact on the result of solving the problem. The paper considers the problem of rendering control, suggesting the formation of the most useful for solving the problem working area of the map when restricted to communications and computing resources. Management is based on the use of knowledge about the construction of useful map orkspaces. The purpose of adaptation is to minimize the deviation of the number of map objects of the workspace from the specified value. Adaptation is based on generating hypotheses and their truth-test. The authors introduce the concept of dialogue, customer and service anomaly. By anomaly is understood the function call of "manual" changing the complexity of the work area. It is considered that in such a case, the knowledge base of service has to be replenished with new design workspace data. The new rules are created after fixing the anomalies. The factors influencing to the observation interval of anomaly are analyzed. The structure of established rules is described, the truth test procedure is considered. Any rule confirmed is included into the knowledge base for a certain period of time. After the expiration of interval the rule transfers to the rank of hypothesis.

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Keywords Visualization of spatial data; geographic information systems; intelligent systems; adaptation.
References 1. Иванников А.Л., Кулагин В.П., Тихонов А.Н., Цветков В.Я. Геоинформатика. – М.: Макс пресс, 2001.
2. Беляков С.Л.,Белякова М.Л., Самойлов Д.С.Геоинформационный сервис ситуационного центра // Информационные технологии. – 2011. – № 8.– С. 29-32.
3. Кобринский Б.А. Значение визуальных образных представлений для медицинских интеллектуальных систем// Искусственный интеллект и принятие решений. – 2012. – № 3. – С. 3-8.
4. Бахин, А. В. Применение концепции облачных вычислений в геоинформационных системах. – Геоинформатика. – 2013. – № 4. – С. 15-18.
5. 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.
6. Jacek MalczewskiGIS-based land-use suitability analysis: a critical overview// Progress in Planning. – 2004. –Vol. 62. – P. 3–65.
7. Keim D.A. Information visualization and visual data mining // IEEE Transactions on Visualization and Computer Graphics. – 2002. – Vol. 8. – P. 1-8.
8. 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. – P. 327-351.
9. 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. – № 2. – Р. 413-431.
10. Wang S A cyberGIS framework for the synthesis of cyberinfrastructure, GIS, and spatial analysis // Annals of the Association of American Geographers. – 2010. – Vol. 100. – P. 535-557.
11. Hart G., Dolbear C. Linked Data: A Geographic Perspective. – Taylor & Francis.–2013.
12. Ahmed Loai Ali, Falko Schmid Data Quality Assurance for Volunteered Geographic Information // Geographic Information ScienceLecture Notes in Computer Science. –2014. – Vol. 8728. –P 126-141.
13. Michael F. Goodchild, Linna Li. Assuring the quality of volunteered geographic information //Spatial Statistics. – 2012. – Vol. 1. – P. 110-120.
14. David Fairbairn MappingDisorder: An Exploratory Study. Modern Trends in Cartography // Lecture Notes in Geoinformation and Cartography. – 2015. – P. 13-22.
15. https://support.google.com/mapmaker/?hl=ru#topic=3180752 (дата обращения 01.09.2014)
16. http://clubs.ya.ru/narod-karta/ (дата обращения 01.09.2014).
17. Popovich V., Vanurin S., Kokh S., Kuzyonny V. Intellectual Geographic Information System for navigation safety // IEEE Aerospace and Electronic Systems Magazine. – 2011. – Vol. 26. – P. 29-31.
18. Беляков С.Л., Диденко Д.А., Самойлов Д.С. Адаптивная процедура управления представлением рабочей области электронной карты // Известия ЮФУ. Технические науки. – 2011. – № 1 (114). – С. 125-130.
19. Беляков С.Л., Розенберг И.Н. Программные интеллектуальные оболочки геоинформационных систем. – М.: Научный мир, 2010.
20. Беляков С.Л., Белякова М.Л., Розенберг И.Н. Ограничения целостности при визуализации пространственной базы данных // Известия ЮФУ. Технические науки.– 2013. – № 5. (142). – С. 138-143.
21. Luger G.F. Artificial Intelligence: Structures and Strategies for Complex Problem Solving. – Addison Wesley. – 2004.
22. Беляков С.Л., Боженюк А.В., Гинис Л.А., Герасименко Е.М. Нечеткие методы управления потоками в геоинформационных системах. – Таганрог. – 2013.
23. Варшавский П.Р., Еремеев А.П. Моделирование рассуждений на основе прецедентов в интеллектуальных системах поддержки принятия решений // Искусственный интеллект и принятие решений. – 2009. – № 1. – С. 45-57.
24. Вагин В.Н., Головина Е.Ю., Загорянская А.А., Фомина М.В. Достоверный и правдоподобный вывод в интеллектуальных системах / Под ред. Вагина В.Н. и Поспелова Д.А. – М.: Физматлит. – 2008.
25. Хорошевский В.Ф.Семантическая интерпретация паттернов данных на основе структурного подхода // Искусственный интеллект и принятие решений. – 2013. – № 2. – С. 3-13.

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