Skip to main content

Decision Support for Wide Area Disasters

  • Chapter
Fusion Methodologies in Crisis Management

Abstract

Information integration processes utilized in a context-aware decision support system for emergency response are considered. The system supports decision making by providing fused outputs of different sources. The chapter demonstrates advantages of ontology-based context to integrate information and to generate useful decisions. A case study concerning a fire response scenario illustrates the system operation. This study focuses on planning fire response actions and evacuation of people in danger using the ride-sharing technology.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    This subsection presents a brief description of the conceptual framework of the CADSS. The detail description can be found in (Smirnov et al. 2005; Smirnov et al. 2015)

References

  • Appriou A, Ayoun A, Benferhat S et al (2001) Fusion: general concepts and characteristics. Int J Intell Syst 16:1107–1134

    Article  MATH  Google Scholar 

  • Baumgartner N et al (2010) BeAware! – Situation awareness, the ontology-driven way. Data Knowl Eng 69:1181–1193

    Article  Google Scholar 

  • Blasch E, Bossé É, Lambert DA (eds) (2012) High-level information fusion management and systems design. Artech House, Boston

    Google Scholar 

  • Dasarathy BV (2001) Information Fusion – what, where, why, when, and how? Information Fusion 2(2):75–76

    Article  Google Scholar 

  • Dey AK (2001) Understanding and using context. Pers Ubiquit Comput 5(1):4–7

    Article  Google Scholar 

  • Garcia J et al (2014) Context-based multi-level information fusion for harbor surveillance. Information Fusion 21(1):173–186

    Google Scholar 

  • Gomez-Romero J, Patricio MA, Garcia J, Molina JM (2011) Ontology-based context representation and reasoning for object tracking and scene interpretation in video. Expert Syst Appl 38(6):7494–7510

    Article  Google Scholar 

  • Haghighat MBA, Aghagolzadeh A, Seyedarabi H (2011) Multi-focus image fusion for visual sensor networks in DCT domain. Comput Electr Eng 37(5):789–797

    Article  MATH  Google Scholar 

  • Holsapple CW, Whinston AB (1986) Building blocks for decision support systems. In: Ariav G, Clifford J (eds) New directions for database systems. Ablex Publishing Corp, Norwood, pp 66–86

    Google Scholar 

  • Honkola J, Laine H, Brown R, Tyrkko O (2010) Smart-M3 information sharing platform. In: Proceedings of IEEE Symposium on Computers and Communications, IEEE Computer Society, pp 1041–1046, doi.ieeecomputersociety.org/10.1109/ISCC.2010.5546642

    Google Scholar 

  • Kennewell JA, Ba-Ngu Vo (2013) An overview of space situational awareness. In: Proceedings of the 16th international conference on information fusion, Istanbul, Turkey, 9–12 July 2013, p 1029–1036

    Google Scholar 

  • Kokar MM, Matheus CJ, Baclawski K (2009) Ontology-based situation awareness. Information Fusion 10(1):83–98. doi:10.1016/j.inffus.2007.0004

    Article  Google Scholar 

  • Landry BC, Mathis BA, Meara NM, Rush JE, Young CE (1970) Definition of some basic terms in computer and information science. J Am Soc Inf Sci 24(5):328–342

    Article  Google Scholar 

  • Masse T, O’Neil S, Rollins J (2008) Information and intelligence (including terrorism) fusion centers. Nova Science Publishers Inc., New York

    Google Scholar 

  • Scherl R, Ulery DL (2004) Technologies for army knowledge fusion. Monmouth University, Computer Science Department, West Long Branch, Monmouth. Final report ARL-TR-3279

    Google Scholar 

  • Smirnov A, Kashevnik A, Shilov N, Balandin S, Oliver I, Boldyrev S (2010) On-the-fly ontology matching in smart spaces: a multi-model approach. In: Smart Spaces and Next Generation Wired/Wireless Networking. Proceedings of the third conference on smart spaces, ruSMART 2010, and the 10th international conference NEW2AN 2010, St. Petersburg, Russia, 23–25 Aug 2010. Lecture notes in computer science, vol 6294, Springer, pp 72–83

    Google Scholar 

  • Smirnov A, Levashova T, Shilov N (2015) Patterns for context-based knowledge fusion in decision support. Inform Fusion 21(1):114–129, http://dx.doi.org/10.1016/j.inffus.2013.10.010

    Article  Google Scholar 

  • Smirnov A, Pashkin M, Chilov N, Levashova T (2005) Constraint-driven methodology for context-based decision support. J Decis Syst 14(3):279–301

    Article  Google Scholar 

  • Smirnov A, Pashkin M, Chilov N, Levashova T, Haritatos F (2003) Knowledge source network configuration approach to knowledge logistics. Int J Gen Syst 32(3):251–269

    Article  MATH  Google Scholar 

  • Steinberg AN, Bowman CL (2013) Adaptive context discovery and exploitation. In: Proceedings of the 16th international conference on information fusion, Istanbul, Turkey, 9–12 July 2013, pp 2004–2011

    Google Scholar 

  • Tsang E (1995) Foundations of constraint satisfaction. Academic, London

    Google Scholar 

  • Waltz EL, Llinas J (1990) Multisensor data fusion. Artech House, Norwood, MA

    Google Scholar 

  • Zins C (2007) Conceptual approaches for defining data, information, and knowledge. J Am Soc Inf Sci Technol 58(4):479–493

    Article  Google Scholar 

Download references

Acknowledgments

The present research was partly supported by the projects funded through grants 12-07-00298, 13-07-00336, 13-07-12095, 13-07-13159, 14-07-00345, 14-07-00427 (the Russian Foundation for Basic Research), the Project 213 (the research program “Information, control, and intelligent technologies & systems” of the Russian Academy of Sciences (RAS)), the Project 2.2 (the Nano- and Information Technologies Branch of RAS), and grant 074-U01 (the Government of the Russian Federation).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexander Smirnov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Smirnov, A., Levashova, T., Shilov, N., Kashevnik, A. (2016). Decision Support for Wide Area Disasters. In: Rogova, G., Scott, P. (eds) Fusion Methodologies in Crisis Management. Springer, Cham. https://doi.org/10.1007/978-3-319-22527-2_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22527-2_25

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22526-5

  • Online ISBN: 978-3-319-22527-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics