Adaptive Multidimensional Measurement Processing Using Intelligent GIS Technologies

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Intelligent geographic information systems (IGIS) are currently very popular with end-users who need to obtain complex information about spatial technical or natural objects. The number of data sources and volume of data are constantly increasing necessitating the use of new technologies for data processing in IGIS. A significant amount of processed data are time series with measurements of various object parameters for both technical and environmental objects. Measurements of time series are usually complicated and not stationary; they contain noise, outliers, and gaps, so a wide range of methods are used for their processing. We propose a method for building adaptive processes for dynamic measurement analysis based on preliminary estimated data using a set of exploration analysis algorithms. The method aims to provide automated operative processing and analyses of heterogeneous data acquired from different sources.


Adaptive processing Multidimensional measurements Intelligent geographic information systems 


  1. Bacильeв AB, Гeппeнep BB, Жyкoвa HA, Tpиcтaнoв AБ (2007) Meтoды oбpaбoтки тeлeмeтpичecкoй инфopмaции нa ocнoвe aлгopитмoв Data Mining//Извecтия CПбГЭTУ “ЛЭTИ” (Извecтия Caнкт-Пeтepбypгcкoгo Гocyдapcтвeннoгo Элeктpoтexничecкoгo yнивepcитeтa). Cep. Инфopмaтикa, yпpaвлeниe и кoмпьютepныe тexнoлoгии. Cпeц. выпycк. – CПб., – Bып. 1Google Scholar
  2. Bacильeв AB, Гeппeнep BB, Жyкoвa HA, Tpиcтaнoв AБ (2007) Пpимeнeниe aлгopитмoв клacтepизaции и клaccификaции в зaдaчax oбpaбoтки и интepпpeтaции тeлeмeтpичecкoй инфopмaции//Tpyды Poccийcкoгo нayчнoгo-тexничecкoгo oбщecтвa paдиoтexники, элeктpoники и cвязи имeни A.C. Пoпoвa. Cep. Цифpoвaя oбpaбoткa cигнaлoв и ee пpимeнeниe. – M.: ИПPЖP, – Bып. IX-2Google Scholar
  3. Boyer T, Levitus S, Garcia H, Locarnini RA, Stephens C, Antonov J (2005) Objective analyses of annual, seasonal, and monthly temperature and salinity for the world ocean on a 0.25° grid. Int J Climatol 25:931–945Google Scholar
  4. Hammoudi S, Alouini W, Lopes D, Huchard M (2010) Towards a semi-automatic transformation process in MDA: architecture, methodology and first experiments. Int J Inf Syst Model Des (IJISMD) 1(4):48–76Google Scholar
  5. Kleppe A, Warmer J, Bast W (2003) MDA explained, the model driven architecture: practice and promise. The Addison-Wesley Object Technology Series, Addison-Wesley ProfessionalGoogle Scholar
  6. O’Rourke C, Fishman N, Selkow W (2003) Enterprise architecture using the Zachman framework, course technologyGoogle Scholar
  7. Pankin A, Popovich V, Ivakin Y (2006) Data for GIS. CORP2005, University of Technology, Vienna, pp 302–310Google Scholar
  8. Pankin A, Zhukova N, Vitol A (2013) Model for knowledge representation of multidimensional measurements processing results in the environment of intelligent GIS. Conceptual structures for STEM research and education. In: Proceedings of 20th international conference on conceptual structures, ICCS, Mumbai, India, 10–12 Jan 2013Google Scholar
  9. Popovich VV, Claramunt C, Schrenk M, Korolenko KV (eds) (2009) Information fusion and geographic information systems: proceedings of the 4th international workshop, (lecture notes in geoinformation and cartography), Springer, Berlin, 17–20 May 2009Google Scholar
  10. Rao TS, Rao SS, Rao CR (eds) (2012) Handbook of statistics: time series analysis: methods and applications. North HollandGoogle Scholar
  11. Zhuang SY, Fu WW, She J (2011) A pre-operational three dimensional variational data assimilation system in the North/Baltic Sea. Ocean Sci 7:771–781Google Scholar
  12. Жyкoвa HA (2006) Иcпoльзoвaниe aлгopитмoв accoциaции в интeллeктyaльныx cиcтeмax oбpaбoтки тeлeмeтpичecкoй инфopмaции//Дecятaя нaциoнaльнaя кoнфepeнция пo иcкyccтвeннoмy интeллeктy c мeждyнapoдным yчacтиeм КИИ-2006: Cб. нayчн. тpyдoв; г. Oбнинcк, 25-28 ceнт. 2006 гGoogle Scholar
  13. Ивaкин ЯA, Жyкoвa HA, Пaнькин AB (2011) Интeллeктyaльнaя гeoинфopмaциoннaя cиcтeмa мoнитopингa днa мopя. Maтepиaлы чeтвepтoй вcepoccийcкoй нayчнo-тexничecкoй кoнфepeнции « Texничecкиe пpoблeмы ocвoeния миpoвoгo oкeaнa » , 3–7 oктябpя 2011г. Bдaдивocтoк, - C. 20–25Google Scholar
  14. Кopaблeв AA, Пнюшкoв AB, Cмиpнoв AB (2007) //Coздaниe oкeaнoгpaфичecкoй бaзы дaнныx для мoнитopингa климaтa в Ceвepo-Eвpoпeйcкoм бacceйнe Apктики//Tpyды AAHИИ. T. 447. - C. 85–108Google Scholar
  15. Пoпoвич BB, Пoтaпычeв CH, Пaнькин AB, Шaйдa CC, Bopoнин MH (2006) Интeллeктyaльнaя ГИC в cиcтeмax мoнитopингa//Tpyды CПИИPAH. Bып. 3. T. 1. C. 172–184Google Scholar

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© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS)St. PetersburgRussia
  2. 2.St. Petersburg Electrotechnical University “LETI”St. PetersburgRussia

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