Skip to main content

Advertisement

SpringerLink
Log in

Towards intelligent GIServices

  • Research Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Distributed information infrastructures are increasingly used in the geospatial domain. In the infrastructures, data are being collected by distributed sensor services, served by distributed geospatial data services, transformed by processing services and workflows, and consumed by smart clients. Consequently, Geographical Information Systems (GISs) are moving from GISystems to GIServices. Intelligent GIServices are enriched with new capabilities including knowledge representation, semantic reasoning, automatic workflow composition, and quality and traceability. Such Intelligent GIServices facilitate information discovery and integration over the network and automate the assembly of GIServices to provide value-added products. This paper provides an overview of intelligent GIServices. The concept of intelligent GIServices is described, followed by a review of the state-of-the-art technologies and methodologies relevant to intelligent GIServices. Visions on how GIServices can perceive, reason, learn, and act intelligently are highlighted. The results can provide better services for big data processing, semantic interoperability, knowledge discovery, and cross-discipline collaboration in Earth science applications.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Aalst W, Hofstede A (2005) YAWL: yet another workflow language. Inf Syst 30(4):245–275

    Article  Google Scholar 

  • Agrawal D, Das S, El Abbadi A (2011) Big data and cloud computing: current state and future opportunities. In Proceedings of the 14th International Conference on Extending Database Technology, ACM, pp 530–533

  • Albrecht J (1997) Universal analytical GIS operations: a task-oriented systematization of data structure-independent GIS functionality. In: Craglia M, Onsrud H (eds) Geographic Information research: transatlantic perspectives. Taylor and Francis, London, pp 577–91

    Google Scholar 

  • Anderson JR (1986) In: Michalski RS, Carbonell JG, Mitchell TM (eds) Machine learning: an artificial intelligence approach, vol 2. Morgan Kaufmann, Burlington

    Google Scholar 

  • Arasu A, Babcock B, Babu S, Cieslewicz J, Datar M, Ito K, Motwani R, Srivastava U, Widom J (2004) STREAM: the Stanford data stream management system. Technical report. Stanford InfoLab. 21pp

  • Aslett M (2011) How will the database incumbents respond to NoSQL and NewSQL. San Francisco, The 451, pp 1–5

  • Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805

    Article  Google Scholar 

  • Baader F, Nutt W (2003) Basic description logics. In: Baader F, Calvanese D, McGuinness D, Nardi D, Patel-Schneider P (eds) The description logic handbook. Theory, implementation and applications. Cambridge University Press, Cambridge, pp 43–95

    Google Scholar 

  • Barker A, Van Hemert J (2008) Scientific workflow: a survey and research directions. In 7th International Conference Parallel Processing and Applied Mathematics, Gdansk, Poland, September 9–12, 2007, Springer Berlin Heidelberg 4967:746–753

  • Barnaghi P, Presser M, Moessner K (2010) Publishing linked sensor data. In CEUR Workshop Proceedings: Proceedings of the 3rd International Workshop on Semantic Sensor Networks (SSN), Organised in conjunction with the International Semantic Web Conference 668

  • Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge

    Google Scholar 

  • Bastin L, Cornford D, Jones R, Heuvelink G, Pebesma E, Stasch C, Nativi S, Mazzetti P, Williams M (2013) Managing uncertainty in integrated environmental modelling: the UncertWeb framework. Environ Model Software 39:116–134

    Article  Google Scholar 

  • Baumann P (2010) The OGC Web Coverage Processing Service (WCPS) standard. Geoinformatica 14(4):447–479

    Article  Google Scholar 

  • Baumann P, Dumitru A, Merticariu V, Misev D, Rusu M (2013) Breaking the big data barrier by enhancing on-board sensor flexibility. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data. ACM 32–36

  • Baumann P, Mazzetti P, Ungar J, Barbera R, Barboni D, Beccati A, Bigagli L, Boldrini E, Bruno R, Calanducci A, Campalani P, Clements O, Dumitru A, Grant M, Herzig P, Kakaletris G, Laxton J, Koltsida P, Lipskoch K, Mahdiraji A R, Mantovani S, Merticariu V, Messina A, Misev D, Natali S, Nativi S, Oosthoek J, Passmore J, Pappalardo M, Rossi A P, Rundo F, Sen M, Sorbera V, Sullivan D, Torrisi M, Trovato L, Veratelli M G, Wagner S (2014) Big data analytics for earth sciences: the EarthServer approach. Int J Digital Earth (submitted)

  • Bernard L, Mäs S, Müller M, Henzen C, Brauner J (2014) Scientific geodata infrastructures: challenges, approaches and directions. Int J Digital Earth 7(7):613–633

    Article  Google Scholar 

  • Berners-Lee T (1998) Semantic Web road map. http://www.w3.org/DesignIssues/Semantic.html. Accessed 31 August 2014

  • Berners-Lee T (2006) Linked Data [Online]. http://www.w3.org/DesignIssues/LinkedData.html. Accessed 31 August 2014

  • Berrick S, Leptoukh G, Farley J, Rui H (2009) Giovanni: a web service workflow-based data visualization and analysis system. IEEE Trans Geosci Remote Sens 47(1):106–113

    Article  Google Scholar 

  • Bhatnagar RK, Kanal LN (1986) Handling uncertain information: a review of numeric and non-numeric methods. In: Kanal LN, Lemmer JF (eds) Uncertainty in AI. Elsevier North-Holland, New York, pp 3–26

    Google Scholar 

  • Bizer C, Heath T, Idehen K, Berners-Lee T (2008) Linked data on the web. In: Proceedings of the 17th international conference on World Wide Web, ACM, Beijing 1265–1266

  • Boccaletti S, Bianconi G, Criado R, Del Genio C I, Gómez-Gardeñes J, Romance M, Zanin M (2014). The structure and dynamics of multilayer networks. Physics Reports

  • Booth D, Haas H, McCabe F, Newcomer E, Champion M, Ferris C, Orchard D (2004) Web services architecture. W3C Working Group Note 11 February 2004, W3C. http://www.w3.org/TR/ws-arch/. Accessed 31 August 2014

  • Botts M, Percivall G, Reed C, Davidson J (2008) OGC® sensor web enablement: overview and high level architecture. In GeoSensor networks, Springer Berlin Heidelberg 175–190

  • Boulos MNK, Warren J, Gong J, Yue P (2010) Web GIS in practice VIII: HTML5 and the canvas element for interactive online mapping. Int J Health Geogr 9(1):14

    Article  Google Scholar 

  • Bradley ES, Roberts DA, Dennison PE, Green RO, Eastwood M, Lundeen SR, Leifer I (2011) Google earth and Google fusion tables in support of time-critical collaboration: mapping the deepwater horizon oil spill with the AVIRIS airborne spectrometer. Earth Sci Inf 4(4):169–179

    Article  Google Scholar 

  • Bröring A, Echterhoff J, Jirka S et al (2011a) New generation sensor web enablement. Sensors 11(3):2652–2699

    Article  Google Scholar 

  • Bröring A, Maué P, Janowicz K, Nüst D, Malewski C (2011b) Semantically-enabled sensor plug & play for the sensor web. Sensors 11(8):7568–7605

    Article  Google Scholar 

  • Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur Gener Comput Syst 25(6):599–616

    Article  Google Scholar 

  • Castronova AM, Goodall JL, Elag MM (2013) Models as web services using the open geospatial consortium (ogc) web processing service (wps) standard. Environ Model Software 41:72–83

    Article  Google Scholar 

  • Cattell R (2011) Scalable SQL and NoSQL data stores. ACM SIGMOD Rec 39(4):12–27

    Article  Google Scholar 

  • Chen H, Chiang RH, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Q 36(4):1165–1188

    Google Scholar 

  • Chen N, Chen X, Wang K, Niu X (2013) Progress and challenges in the architecture and service pattern of earth observation sensor Web for digital earth. Int J Digital Earth 17. doi:10.1080/17538947.2013.834385

  • Cios KJ, Pedrycz W, Swiniarsk RM (1998) Data mining methods for knowledge discovery. IEEE Trans Neural Netw 9(6):1533–1534

    Article  Google Scholar 

  • Clark DA (1990) Numerical and symbolic approaches to uncertainty management in AI. Artif Intell Rev 4:109–146

    Article  Google Scholar 

  • Condie T, Mineiro P, Polyzotis N, Weimer M (2013) Machine learning for big data. In Proceedings of the 2013 international conference on Management of data, ACM 939–942

  • Cox S, Schade S (2010) Linked data: what does it offer earth sciences?. In EGU General Assembly Conference Abstracts 12:2079

  • de Jesus J, Walker P, Grant M, Groom S (2012) WPS orchestration using the Taverna workbench: the eScience approach. Comput Geosci 47:75–86

    Article  Google Scholar 

  • Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113

    Article  Google Scholar 

  • Di L (2007) Geospatial sensor web and self-adaptive Earth predictive systems (SEPS). In Proceedings of the Earth Science Technology Office (ESTO)/Advanced Information System Technology (AIST) Sensor Web Principal Investigator (PI) Meeting, San Diego, USA, pp. 1–4

  • Di L, Moe K, van Zyl TL (2010) Earth observation sensor web: an overview. IEEE J Sel Top Appl Earth Obs Remote Sens 3(4):415–417

    Article  Google Scholar 

  • Di L, Yue P, Ramapriyan HK, King RL (2013) Geoscience data provenance: an overview. IEEE Trans Geosci Remote Sens 51(11):5065–5072

    Article  Google Scholar 

  • Dumitru A, Merticariu V, Baumann P (2014) exploring cloud opportunities from an array database perspective. Proc ACM SIGMOD workshop on data analytics in the cloud (DanaC’2014), June 22–27, 2014, Snowbird, USA 1–4

  • Echterhoff J, Everding T (2011) OGC Event service-review and current state. Version . OGC 11-088r1. Open Geospatial Consortium, Inc. 33 pp

  • Egenhofer MJ (2002) Toward the semantic geospatial Web. In Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems, Mclean, VA, USA, 8–9 November 2002, pp 1–4

  • Evangelidis K, Ntouros K, Makridis S, Papatheodorou C (2014) Geospatial services in the cloud. Comput Geosci 63:116–122

    Article  Google Scholar 

  • Fan M, Fan H, Chen N, Chen Z, Du W (2013) Active on-demand service method based on event-driven architecture for geospatial data retrieval. Comput Geosci 56:1–11

    Article  Google Scholar 

  • Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37

    Google Scholar 

  • Fielding RT (2000) Architectural styles and the design of network-based software architectures. Dissertation, University of California, http://www.ics.uci.edu/~fielding/pubs/dissertation/top.htm. Accessed 31 August 2014

  • Filipponi L, Vitaletti A, Landi G, Memeo V, Laura G, Pucci P (2010) Smart city: an event driven architecture for monitoring public spaces with heterogeneous sensors. In 2010 Fourth International Conference on Sensor Technologies and Applications (SENSORCOMM), IEEE, pp 281–286

  • Foerster T, Brühl A, Schäffer B (2011) RESTful web processing service. In Proceedings 14th AGILE International Conference on Geographic Information Science, Utrecht, Netherlands

  • Frew J, Bose R (2001) Earth system science workbench: a data management infrastructure for earth science products. Proc Thirteenth Int Conf Sci Stat Database Manag IEEE 180–189

  • Gao S, Mioc D, Yi X (2009) The measurement of geospatial Web service quality in SDIs. In: Proceedings 17th International Conference on Geoinformatics, Fairfax, USA pp 1–6

  • Garijo D, Gil Y (2011) A new approach for publishing workflows: abstractions, standards, and linked data. In Proceedings of the 6th workshop on Workflows in support of large-scale science, ACM, pp 47–56

  • GeoPW (2013) GeoPW. http://sourceforge.net/projects/geopw/, accessed August 31, 2014

  • Gil Y, Ratnakar V, Kim J, González-Calero PA, Groth P, Moody J, Deelman E (2011) Wings: intelligent workflow-based design of computational experiments. IEEE Intell Syst 26(1):62–72

    Article  Google Scholar 

  • Gil Y, Greaves M, Hendler J, Hirsh H (2014) Amplify scientific discovery with artificial intelligence. Science 346(6206):171–172

    Article  Google Scholar 

  • Gong J, Xiang L, Chen J, Yue P, Liu Y (2011) GeoGlobe: a virtual globe for multi-source geospatial information integration and service. Advances in Web-based GIS, Mapping Services and Applications, Taylor & Francis Publication

  • Goodchild MF, Guo H, Annoni A, Bian L, de Bie K, Campbell F, Woodgate P (2012) Next-generation digital earth. Proc Natl Acad Sci 109(28):11088–11094

    Article  Google Scholar 

  • Granell C (2014) Robust workflow systems + flexible geoprocessing services = geo-enabled model Web? In: Pourabbas E (ed) Geographical information systems: trends and technologies. CRC Press, Boca Raton, pp 172–204

    Google Scholar 

  • Granell C, Fernández ÓB, Díaz L (2014) Geospatial information infrastructures to address spatial needs in health: collaboration, challenges and opportunities. Futur Gener Comput Syst 31:213–222

    Article  Google Scholar 

  • Gunay A, Akcay O, Altan MO (2014) Building a semantic based public transportation geoportal compliant with the INSPIRE transport network data theme. Earth Sci Inf 7(1):25–37

    Article  Google Scholar 

  • Gyrard A, Bonnet C, Boudaoud K (2014) Enrich machine-to-machine data with semantic web technologies for cross-domain applications. In 2014 I.E. World Forum on Internet of Things (WF-IoT), IEEE 559–564

  • Han J, Haihong E, Le G, Du J (2011) Survey on NoSQL databases. In 6th international conference on Pervasive computing and applications (ICPCA), IEEE 363–366

  • Hatzi O, Vrakas D, Nikolaidou M, Bassiliades N, Anagnostopoulos D, Vlahavas L (2012) An integrated approach to automated semantic web service composition through planning. IEEE Trans Serv Comput 5(3):319–332

    Article  Google Scholar 

  • He L, Yue P, Di L, Zhang M, Hu L (2015a) Adding geospatial data provenance into SDI—a service-oriented approach. IEEE J Sel Top Appl Earth Obs Remote Sens 8(2):926–936

    Article  Google Scholar 

  • He L, Yue P, Jiang L, Zhang M (2015b) Fuzzy spatial relation ontology driven detection of complex geospatial features in a web service environment. Earth Sci Inf 8(1):63–76

    Article  Google Scholar 

  • Hendler J (2003) Science and the semantic web. Science 299(5606):520–521

    Article  Google Scholar 

  • Hey T, Trefethen AE (2005) Cyberinfrastructure for e-science. Science 308(5723):817–821

    Article  Google Scholar 

  • Hofer B (2013) Geospatial cyberinfrastructure and geoprocessing Web—a review of commonalities and differences of e-science approaches. ISPRS Int J Geogr Inf 2(3):749–765

    Article  Google Scholar 

  • Hopgood AA (2011) Intelligent systems for engineers and scientists. CRC press, Boca Raton

    Google Scholar 

  • INSPIRE (2008) INSPIRE Network Services Architecture 30

  • Janowicz K, Bröring A, Stasch C, Schade S, Everding T, Llaves A (2013) A restful proxy and data model for linked sensor data. Int J Digital Earth 6(3):233–254

    Article  Google Scholar 

  • Jara AJ, Olivieri AC, Bocchi Y, Jung M, Kastner W, Skarmeta AF (2014) Semantic Web of things: an analysis of the application semantics for the IoT moving towards the IoT convergence. Int J Web Grid Serv 10(2):244–272

    Article  Google Scholar 

  • Ji C, Li Y, Qiu W, Awada U, Li K (2012) Big data processing in cloud computing environments. In Proceedings of the 2012 12th International Symposium on Pervasive Systems, Algorithms and Networks. IEEE Comput Soc 17–23

  • Jordan D, Evdemon J (2007) Web services business process execution language. OASIS, version 2.0 http://docs.oasis-open.org/wsbpel/2.0/wsbpel-v2.0.pdf. Accessed 31 August 2014

  • Jungmann A, Kleinjohann B (2012) Towards the application of reinforcement learning techniques for quality-based service selection in automated service composition. In 2012 I.E. Ninth International Conference on Services Computing (SCC), IEEE, pp 701–702

  • Kadner K, Oberle D (2011) Unified service description language XG final report, W3C Incubator Group Report 27 October 2011. http://www.w3.org/2005/Incubator/usdl/XGR-usdl-20111027/. Accessed 31 Augest 2013

  • Karimi HA (Ed.) (2014). Big Data: techniques and technologies in geoinformatics. CRC Press

  • Karpagam GR, Bhuvaneswari A (2011) AI planning-based semantic web service composition. Int J Innov Comput Appl 3(3):126–135

    Article  Google Scholar 

  • Katasonov A, Kaykova O, Khriyenko O, Nikitin S, Terziyan VY (2008) Smart semantic middleware for the internet of things. ICINCO-ICSO 8:169–178

  • Kouicem A, Chibani A, Tari A, Amirat Y, Tari Z (2014) Dynamic services selection approach for the composition of complex services in the web of objects. In 2014 I.E. World Forum on Internet of Things (WF-IoT), IEEE, pp 298–303

  • Krishnakumar K (2003) Intelligent systems for aerospace engineering - an overview. National Aeronautics and space administration Moffett Field CA Ames Research Center

  • Kuzu M, Cicekli NK (2012) Dynamic planning approach to automated web service composition. Appl Intell 36(1):1–28

    Article  Google Scholar 

  • Lacasta J, Nogueras-Iso J, Béjar R, Muro-Medrano PR, Zarazaga-Soria FJ (2007) A Web ontology service to facilitate interoperability within a spatial data infrastructure: applicability to discovery. Data Knowl Eng 63(3):947–971

    Article  Google Scholar 

  • Lacoche J, Duval T, Arnaldi B, et al (2014) A survey of plasticity in 3D user interfaces, 7th Workshop on Software Engineering and Architectures for Realtime Interactive Systems

  • Laniak GF, Olchin G, Goodall J, Voinov A, Hill M, Glynn P, Whelan G, Geller G, Quinn N, Blind M, Peackham S, Reaney S, Gaber N, Kennedy R, Hughes A (2013) Integrated environmental modeling: a vision and roadmap for the future. Environ Model Software 39:3–23

    Article  Google Scholar 

  • Lanter DP (1991) Design of a lineage-based meta-data base for GIS. Cartogr Geogr Inf Syst 18(4):255–261

    Article  Google Scholar 

  • Li S, Da XL, Zhao S (2014a) The internet of things: a survey. Inf Syst Front. doi:10.1007/s10796-014-9492-7

    Google Scholar 

  • Li L, Li S, Zhao S (2014b) QoS-aware scheduling of services-oriented internet of things. IEEE Trans Ind Inf 10(2):1497–1505

    Article  Google Scholar 

  • Maguire D, Longley P (2005) The emergence of geoportals and their role in spatial data infrastructures. Comput Environ Urban Syst 29(1):3–14

    Article  Google Scholar 

  • Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers A (2011) Big data: The next frontier for innovation, Competition, and Productivity. McKinsey Global Institute

  • Maué P, Michels H, Roth M (2012) Injecting semantic annotations into (geospatial) web service descriptions. Semant Web J 3(4):385–395

    Google Scholar 

  • Mazzetti P, Nativi S, Caron J (2009) RESTful implementation of geospatial services for earth and space science applications. Int J Digital Earth 2(S1):40–61

    Article  Google Scholar 

  • McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133

    Article  Google Scholar 

  • McFerren G, Van Zyl T, Vahed A (2012) FOSS geospatial libraries in scientific workflow environments: experiences and directions. Appl Geomatics 4(2):85–93

    Article  Google Scholar 

  • Michelson BM (2006) Event-driven architecture overview. Patricia Seybold Group

  • Michener WK, Allard S, Budden A et al (2012) Participatory design of DataONE—enabling cyberinfrastructure for the biological and environmental sciences. Ecol Inf 11:5–15

    Article  Google Scholar 

  • Misev D, Baumann P (2014) Extending the SQL array concept to support scientific analytics. Proc. Intl. Conf. on Scientific and Statistical Database Management (SSDBM’2014), June 30 - July 2, 2014, Aalborg, Denmark, paper #10

  • Moses R (2011) Enabling quality of geospatial Web services. In: Zhao P, Di L (eds.) Geospatial Web services: advances in information interoperability. IGI Global, Hershey, pp 33–63

  • Müller M, Bernard L, Brauner J (2010) Moving code in spatial data infrastructures–web service based deployment of geoprocessing algorithms. Trans GIS 14(s1):101–118

    Article  Google Scholar 

  • Müller M, Bernard L, Kadner D (2013) Moving code–sharing geoprocessing logic on the Web. ISPRS J Photogramm Remote Sens 83:193–203

    Article  Google Scholar 

  • Nativi S, Craglia M, Pearlman J (2013a) Earth science infrastructures interoperability: the brokering approach. IEEE J Sel Top Appl Earth Obs Remote Sens 6(3):1118–1129

    Article  Google Scholar 

  • Nativi S, Mazzetti P, Geller G (2013b) Environmental model access and interoperability: the GEO model Web initiative. Environ Model Software 39:214–228

    Article  Google Scholar 

  • Negnevitsky M (2005) Artificial intelligence: a guide to intelligent systems. Pearson Education, Upper Saddle River

    Google Scholar 

  • Neumeyer L, Robbins B, Nair A, Kesari A (2010) S4: Distributed stream computing platform. In 2010 I.E. International Conference on Data Mining Workshops (ICDMW), IEEE, pp 170–177

  • Ngan LD, Kanagasabai R (2013) Semantic Web service discovery: state-of-the-art and research challenges. Pers Ubiquit Comput 17(8):1741–1752

    Article  Google Scholar 

  • NIST (2011) National Institute of Standards and Technology (NIST) Definition of Cloud Computing. http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf. Accessed December 5, 2014

  • NSF (2007) Cyberinfrastructure vision for 21st Century Discovery, National Science Foundation, USA, March, 2007, 64 pp

  • NSF (2014) EarthCube: Past, Present, and Future. http://earthcube.org/document/earthcube-past-present-future. Accessed 26 March 2015

  • O’Reilly T (2005) What is Web 2.0. http://oreilly.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html. Accessed 31 August 2014

  • OGC (2014) Open Geospatial Consortium, www.opengeospatial.org. Accessed 31 August 2014

  • OGC (2015) 3D Portrayal Service Candidate Standard, OGC 15–001, Open Geospatial Consortium

  • Onchaga R (2004) Modelling for quality of services in distributed geoprocessing. In: Proceedings XXth ISPRS Congress, Istanbul, Turkey, pp 212–217

  • Page KR, De Roure DC, Martinez K, Sadler JD, Kit OY (2009) Linked sensor data: Restfully serving rdf and gml. Proc Semant Sens Netw 49

  • Paik I, Chen W, Kumara BT, Tanaka T, Li Z, Yaguchi Y (2014) Linked data-based service publication for service clustering. In Advanced in Computer Science and its Applications. Springer Berlin Heidelberg, pp 1429–1435

  • Papazoglou MP (2003) Service-oriented computing: concepts, characteristics and directions. In Proceedings of the Fourth International Conference on Web Information Systems Engineering (WISE), IEEE, pp 3–12

  • Patni H, Sahoo S, Henson C, Sheth A (2010) Provenance aware linked sensor data. In Proceedings of the second workshop on trust and privacy on the social and semantic web

  • Perry M, Herring J (2012) OGC GeoSPARQL - a geographic query language for RDF data. Version 1.0, OGC 11-052r4, Open Geospatial Consortium, Inc

  • Pistore M, Barbon F, Bertoli P, Shaparau D, Traverso P (2004) Planning and monitoring web service composition. In artificial intelligence: methodology, systems, and applications. Springer, Berlin, pp 106–115

    Book  Google Scholar 

  • Pouchard LC, Branstetter ML, Cook RB, Devarakonda R, Green J, Palanisamy G, Noy NF (2013) A linked science investigation: enhancing climate change data discovery with semantic technologies. Earth Sci Inf 6(3):175–185

    Article  Google Scholar 

  • Pratt A, Peters C, Siddeswara G, Lee B, Terhorst A (2010) Exposing the Kepler scientific workflow system as an OGC web processing service. Proceedings of iEMSs (International Environmental Modelling and Software Society)

  • Royan J, Gioia P, Cavagna R et al (2007) Network-based visualization of 3d landscapes and city models. IEEE Comput Graph Appl 27(6):70–79

    Article  Google Scholar 

  • Rudas IJ, Fodor J (2008) Intelligent systems. Int J Comput Commun Control 3(Suppl):132–138

    Google Scholar 

  • Russell S, Norvig P (1995) Artificial intelligence: a modern approach. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Schadt EE, Linderman MD, Sorenson J, Lee L, Nolan GP (2010) Computational solutions to large-scale data management and analysis. Nat Rev Genet 11(9):647–657

    Article  Google Scholar 

  • Scharl A, Tochtermann K (2009) The geospatial web: how geobrowsers, social software and the Web 2.0 are shaping the network society. Springer, London

    Google Scholar 

  • Sheth A, Henson C, Sahoo SS (2008) Semantic sensor web. IEEE Internet Comput 12(4):78–83

    Article  Google Scholar 

  • Simonis I, Dibner P (2007) OpenGIS sensor planning service implementation specification. Implementation specification OGC

  • Slavakis K, Giannakis G, Mateos G (2014) Modeling and optimization for big data analytics: (Statistical) learning tools for our era of data deluge. IEEE Signal Process Mag 31(5):18–31

    Article  Google Scholar 

  • ITU Strategy and Policy Unit (SPU) (2005) ITU internet reports 2005: the internet of things. Geneva: International Telecommunication Union (ITU)

  • Sun Z, Yue P, Di L (2012) GeoPWTManager: a task-oriented web geoprocessing system. Comput Geosci 47:34–45

    Article  Google Scholar 

  • Tamayo A, Granell C, Huerta J (2012) Measuring complexity in OGC web services XML schemas: pragmatic use and solutions. Int J Geogr Inf Sci 26(6):1109–1130

    Article  Google Scholar 

  • Tao F, Cheng Y, Xu LD, Zhang L, Li BH (2014) CCIoT-CMfg: cloud computing and internet of things based cloud manufacturing service system. IEEE Trans Ind Inf 10(2):1435–1442

    Article  Google Scholar 

  • TIANDITU (2014) TIANDITU MapWorld. http://www.tianditu.cn, accessed August 31, 2014

  • Tilmes C, Fleig AJ (2008) Provenance tracking in an earth science data processing system. In Provenance and Annotation of Data and Processes. Springer Berlin Heidelberg, pp 221–228

  • Toshniwal A, Taneja S, Shukla A, Ramasamy K, Patel JM, Kulkarni S, Jackson J, Gade K, Fu M, Donham J, Bhagat N, Mittal S, Ryaboy D (2014) Storm@ twitter. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data, ACM, pp 147–156

  • Vaccari L, Craglia M, Fugazza C, Nativi S, Santoro M (2012) Integrative research: the EuroGEOSS experience. IEEE J Sel Top Appl Earth Obs Remote Sens 5(6):1603–1611

    Article  Google Scholar 

  • van Zyl TL, Vahed A, McFerren G, Hohls D (2012) Earth observation scientific workflows in a distributed computing environment. Trans GIS 16(2):233–248

    Article  Google Scholar 

  • Viriyasitavat W, Xu L (2014) Compliance checking for requirement-oriented service workflow interoperations. IEEE Trans Ind Inf. doi:10.1109/TII.2014.2301132

    Google Scholar 

  • VoCamp (2015) http://vocamp.org/wiki/Main_Page, accessed March 26, 2015

  • Vokrinek J, Komenda A, Pechoucek M (2011) Abstract architecture for task-oriented multi-agent problem solving. IEEE Trans Syst Man Cybern Part C Appl Rev 41(1):31–40

    Article  Google Scholar 

  • W3C (2015) http://www.w3.org/2015/spatial/charter, accessed March 26, 2015

  • Wang H, Zhou X, Zhou X, Liu W, Li W, Bouguettaya A (2010) Adaptive service composition based on reinforcement learning. SOC 6470:92–107

    Google Scholar 

  • Wang X, Yu J, Baumann P (2011) A Web coverage ontology for geospatial Web applications. Proc. IEEE Intl. Conf. on Semantic Computing (ICSC’2011), Palo Alto, USA, September 18–21, 2011, pp. 216–223

  • Wang S, Anselin L, Bhaduri B, Crosby C, Goodchild MF, Liu Y, Nyerges TL (2013) CyberGIS software: a synthetic review and integration roadmap. Int J Geogr Inf Sci 27(11):2122–2145

    Article  Google Scholar 

  • WfMC (2008) Process definition interface—XML process definition language. Workflow Management Coalition, Cohasset, p 217

  • White T (2009) Hadoop: the definitive guide: the definitive guide. O’Reilly Media, Sebastopol

    Google Scholar 

  • Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Burlington

    Google Scholar 

  • Wright DJ, Wang S (2011) The emergence of spatial cyberinfrastructure. Proc Natl Acad Sci 108(14):5488–5491

    Article  Google Scholar 

  • Yang C, Raskin R, Goodchild M, Gahegan M (2010) Geospatial cyberinfrastructure: past, present and future. Comput Environ Urban Syst 34:264–277

    Article  Google Scholar 

  • Yang C, Xu Y, Nebert D (2013) Redefining the possibility of digital earth and geosciences with spatial cloud computing. Int J Digital Earth 6(4):297–312

    Article  Google Scholar 

  • Yuan J, Yue P, Gong J, Zhang M (2013) A linked data approach for geospatial data provenance. IEEE Trans Geosci Remote Sens 51(11):5105–5112

    Article  Google Scholar 

  • Yue P, Di L, Yang W, Yu G, Zhao P (2007) Semantics-based automatic composition of geospatial Web services chains. Comput Geosci 33(5):649–665

    Article  Google Scholar 

  • Yue P, Di L, Yang W, Yu G, Zhao P, Gong J (2009) Semantic web services—based process planning for earth science applications. Int J Geogr Inf Sci 23(9):1139–1163

    Article  Google Scholar 

  • Yue P, Gong J, Di L, Yuan J, Sun L, Sun Z, Wang Q (2010) GeoPW: laying blocks for the geospatial processing web. Trans GIS 14(6):755–772

    Article  Google Scholar 

  • Yue P, Gong J, Di L, He L, Wei Y (2011a) Integrating semantic web technologies and geospatial catalog services for geospatial information discovery and processing in cyberinfrastructure. Geoinformatica 15(2):273–303

    Article  Google Scholar 

  • Yue P, Wei Y, Di L, He L, Gong J, Zhang L (2011b) Sharing geospatial provenance in a service-oriented environment. Comput Environ Urban Syst 35(4):333–343

    Article  Google Scholar 

  • Yue P, Di L, Wei Y, Han W (2013a) Intelligent services for discovery of complex geospatial features from remote sensing imagery. ISPRS J Photogramm Remote Sens 83:151–164

    Article  Google Scholar 

  • Yue P, Zhou H, Gong J, Hu L (2013b) Geoprocessing in cloud computing platforms—a comparative analysis. Int J Digital Earth 6(4):404–425

    Article  Google Scholar 

  • Yue P, Jiang L, Hu L (2014a) Google fusion tables for managing soil moisture sensor observations. IEEE J Sel Top Appl Earth Obs Remote Sens 7(11):4414–4421

    Article  Google Scholar 

  • Yue P, Tan Z, Zhang M (2014b) GeoQoS: delivering quality of services on the Geoprocessing Web. In: Proceedings of OSGeo’s European Conference on Free and Open Source Software for Geospatial (FOSS4G-Europe 2014), 15–17 July 2014, Bremen, Germany, 11pp

  • Yue P, Zhang M, Tan Z (2015) A geoprocessing workflow system for environmental monitoring and integrated modelling. Environ Model Software 69:128--140

  • Zhao P, Di L (2010) Geospatial Web services: advances in information interoperability. IGI Global publisher, Hershey, p 552

    Google Scholar 

  • Zhao P, Foerster T, Yue P (2012a) The geoprocessing web. Comput Geosci 47(10):3–12

    Article  Google Scholar 

  • Zhao P, Di L, Han W, Li X (2012b) Building a Web-services based geospatial online analysis system. IEEE J Sel Top Appl Earth Obs Remote Sens 5(6):1780–1792

    Article  Google Scholar 

  • Zhou M, Ma Y (2013) QoS-aware computational method for IoT composite service. J China Univ Posts Telecommun 20:35–39

    Article  Google Scholar 

  • Zikopoulos P, Eaton C (2011) Understanding big data: analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media, New York

    Google Scholar 

Download references

Acknowledgments

We are grateful to Dr. Rahul Ramachandran and anonymous reviewers for their constructive comments and suggestions. The work was supported by National Basic Research Program of China (2011CB707105), National Natural Science Foundation of China (91438203 and 41271397), Hubei Science and Technology Support Program (2014BAA087),Program for New Century Excellent Talents in University (NCET-13-0435), and Fundamental Research Funds for the Central Universities (2042014kf0224).

Author information

Authors and Affiliations

  1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, 129 Luoyu Road, Wuhan, Hubei, 430079, China

    Peng Yue & Liangcun Jiang

  2. Jacobs University Bremen, Bremen, Germany

    Peter Baumann

  3. University of Alabama in Huntsville, Huntsville, AL, 35899, USA

    Kaylin Bugbee

Authors
  1. Peng Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Baumann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kaylin Bugbee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liangcun Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Yue.

Additional information

Communicated by: H. A. Babaie

Published in the Special Issue of Intelligent GIServices with Guest Editors Dr. Peng Yue, Dr. Rahul Ramachandran and Dr. Peter Baumann

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yue, P., Baumann, P., Bugbee, K. et al. Towards intelligent GIServices. Earth Sci Inform 8, 463–481 (2015). https://doi.org/10.1007/s12145-015-0229-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-015-0229-z

Keywords

Search

Navigation