Advertisement

Towards an Environmental Decision-Making System: A Vocabulary to Enrich Stream Data

  • Peter Wetz
  • Tuan-Dat Trinh
  • Ba-Lam Do
  • Amin Anjomshoaa
  • Elmar Kiesling
  • A Min Tjoa
Part of the Progress in IS book series (PROIS)

Abstract

The future of the earth’s environmental systems will, to a major extent, be determined in cities, where already more than 50 % of the human population is concentrated. Pervasively available sensors and the data they generate can help to address pressing environmental challenges in urban areas by making crucial information available to researchers and decision-makers. However, environmental data is at present typically stored in disparate systems and formats, which inhibits reuse and integration. Furthermore, the large amounts of environmental data that stream in continuously require novel processing approaches. So far, research at the intersection of environmental sciences and urban data infrastructures has been scarce. To address these issues, we develop a novel framework based on semantic web technologies. We apply data modeling and semantic stream processing technologies in order to facilitate integration, comparison, and visualization of heterogeneous data from various sources. This paper presents the concept of a platform for environmental data stream analysis, and focuses on the design of a new vocabulary to semantically enrich the processed streams. The implemented architecture shall be capable of informing and supporting decision-making by non-expert users. We propose and discuss a three-step framework, present a vocabulary to model environmental data streams, and outline initial results.

Keywords

Environmental data streams Semantic sensor network ontology RDF data cube vocabulary Stream processing 

References

  1. 1.
    D. Anicic et al., “EP-SPARQL: A Unified Language for Event Processing and Stream Reasoning,” in Proc. of the 20th International Conference on World Wide Web, New York, NY, USA, 2011, pp. 635–644.Google Scholar
  2. 2.
    M. Balduini et al., “Social listening of city scale events using the streaming linked data framework,” in The Semantic Web – ISWC 2013 – 12th International Semantic Web Conference Proceedings Part II, Springer, 2013, pp. 1–16.Google Scholar
  3. 3.
    D. F. Barbieri et al., “Querying RDF Streams with C-SPARQL,” SIGMOD Record, vol. 39, no. 1, 2010, pp. 20–26.CrossRefGoogle Scholar
  4. 4.
    M. Cáceres, “Packaged Web Apps (Widgets) – Packaging and XML Configuration,” W3C Recomm., 2012.Google Scholar
  5. 5.
    J.-P. Calbimonte et al., “Enabling Ontology-based Access to Streaming Data Sources,” in The Semantic Web – ISWC 2010 – 9th International Semantic Web Conference Part I, 2010, pp. 96–111.Google Scholar
  6. 6.
    M. Compton et al., “The SSN ontology of the W3C semantic sensor network incubator group,” Web Semantics: Science, Services and Agents on the World Wide Web, vol. 17, 2012, pp. 25–32.CrossRefGoogle Scholar
  7. 7.
    Directive number 4 of 2003, OJ of the EU, L 41, pp. 26–32, 2003.Google Scholar
  8. 8.
    P. T. Eugster et al., “The many faces of publish/subscribe,” ACM Comput. Surv. CSUR, vol. 35, no. 2, 2003, pp. 114–131.CrossRefGoogle Scholar
  9. 9.
    G. Huang and N. Chang, “The perspectives of environmental informatics and systems analysis,” J. Environ. Inform., vol. 1, no. 1, 2003, pp. 1–7.CrossRefGoogle Scholar
  10. 10.
    F. Lécué et al., “Predicting Severity of Road Traffic Congestion Using Semantic Web Technologies,” in Proc. of the 11th Extended Semantic Web Conf., 2014, pp. 611–627.Google Scholar
  11. 11.
    L. Lefort et al., “A linked sensor data cube for a 100 year homogenised daily temperature dataset,” in 5th International Workshop on Semantic Sensor Networks (SSN-2012), CEUR-Proceedings, vol. 904, 2012, pp. 1–16.Google Scholar
  12. 12.
    L. Lefort et al., “The ACORN-SAT Linked Climate Dataset,” 2013.Google Scholar
  13. 13.
    D. Le-Phuoc et al., “A Native and Adaptive Approach for Unified Processing of Linked Streams and Linked Data,” in The Semantic Web – ISWC 2011 – 10th International Semantic Web Conference Part I, 2011, pp. 370–388.Google Scholar
  14. 14.
    R. G. Raskin and M. J. Pan, “Knowledge representation in the semantic web for Earth and environmental terminology (SWEET),” Comput. Geosci., vol. 31, no. 9, 2005, pp. 1119–1125.CrossRefGoogle Scholar
  15. 15.
    B. Resch et al., “Towards the live city–paving the way to real-time urbanism,” Int. J. Adv. Intell. Syst., vol. 5, no. 3 and 4, 2012, pp. 470–482.Google Scholar
  16. 16.
    M. Rinne et al., “INSTANS: High-Performance Event Processing with Standard RDF and SPARQL,” in Proc. of the ISWC 2012 Posters & Demonstrations Track, 2012, vol. 914.Google Scholar
  17. 17.
    H. J. Schellnhuber et al., “World in transition: a social contract for sustainability,” German Advisory Council on Global Change, 2011.Google Scholar
  18. 18.
    M. Sporny et al., “JSON-LD 1.0 – A JSON based Serialization for Linked Data,” W3C Recomm., 2014.Google Scholar
  19. 19.
    M. Stocker et al., “Towards an Ontology for Situation Assessment in Environmental Monitoring,” in Proc. of the 7th International Congress on Environmental Modelling and Software, 2014, pp. 1281–1288.Google Scholar
  20. 20.
    S. Tallevi-Diotallevi et al., “Real-Time Urban Monitoring in Dublin Using Semantic and Stream Technologies,” in The Semantic Web – ISWC 2013 – 12th International Semantic Web Conference Proceedings Part II, 2013, pp. 178–194.Google Scholar
  21. 21.
    T. Tarasova et al., “Semantically-Enabled Environmental Data Discovery and Integration: Demonstration Using the Iceland Volcano Use Case,” in Knowledge Engineering and the Semantic Web, 2013, pp. 289–297.Google Scholar
  22. 22.
    T.-D. Trinh et al., “Linked Widgets-An Approach to Exploit Open Government Data,” in Proc. of the 15th Int. Conf. on Information Integration and Web-based Applications & Services, 2013, pp. 438–442.Google Scholar
  23. 23.
    T.-D. Trinh et al., “Open Linked Widgets Mashup Platform,” in Proceedings of the AI Mashup Challenge 2014 (ESWC Satellite Event), 2014, p. 9.Google Scholar
  24. 24.
    P. Wetz et al., “Towards an Environmental Information System for Semantic Stream Data,” in 28th International Conf. on Informatics for Environmental Protection: ICT for Energy Efficiency, EnviroInfo 2014, 2014, pp. 637–644.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Peter Wetz
    • 1
  • Tuan-Dat Trinh
    • 1
  • Ba-Lam Do
    • 1
  • Amin Anjomshoaa
    • 1
  • Elmar Kiesling
    • 1
  • A Min Tjoa
    • 1
  1. 1.Institute of Software Technology and Interactive SystemsTU WienViennaAustria

Personalised recommendations