Decision Support for Wide Area Disasters

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.

[1]  Werner Retschitzegger,et al.  BeAware! - Situation awareness, the ontology-driven way , 2010, Data Knowl. Eng..

[2]  Rudolf Kruse,et al.  Fusion: General concepts and characteristics , 2001, Int. J. Intell. Syst..

[3]  Mieczyslaw M. Kokar,et al.  Ontology-based situation awareness , 2009, Inf. Fusion.

[4]  Eloi Bosse,et al.  High-Level Information Fusion Management and System Design , 2012 .

[5]  Alexander V. Smirnov,et al.  Patterns for context-based knowledge fusion in decision support systems , 2015, Inf. Fusion.

[6]  Alexander V. Smirnov,et al.  Knowledge source network configuration approach to knowledge logistics , 2003, Int. J. Gen. Syst..

[7]  Richard B. Scherl,et al.  Technologies for Army Knowledge Fusion , 2004 .

[8]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[9]  Ba-Ngu Vo,et al.  An overview of space situational awareness , 2013, Proceedings of the 16th International Conference on Information Fusion.

[10]  Ronald Brown,et al.  Smart-M3 information sharing platform , 2010, The IEEE symposium on Computers and Communications.

[11]  Alexander V. Smirnov,et al.  Constraint-driven Methodology for Context-based Decision Support , 2005, J. Decis. Syst..

[12]  Alexander V. Smirnov,et al.  On-the-Fly Ontology Matching in Smart Spaces: A Multi-model Approach , 2010, NEW2AN.

[13]  Alain Appriou.  Situation assessment based on spatially ambiguous multisensor measurements , 2001, Int. J. Intell. Syst..

[14]  Chaim Zins,et al.  Conceptual approaches for defining data, information, and knowledge , 2007, J. Assoc. Inf. Sci. Technol..

[15]  James Llinas,et al.  Multisensor Data Fusion , 1990 .

[16]  Edward P. K. Tsang,et al.  Foundations of constraint satisfaction , 1993, Computation in cognitive science.

[17]  Belur V. Dasarathy,et al.  Information Fusion - what, where, why, when, and how? Editorial , 2001, Inf. Fusion.

[18]  Hadi Seyedarabi,et al.  Multi-focus image fusion for visual sensor networks in DCT domain , 2011, Comput. Electr. Eng..

[19]  B. C. Landry,et al.  Definition of some basic terms in computer and information science , 1973, J. Am. Soc. Inf. Sci..

[20]  Miguel A. Patricio,et al.  Ontology-based context representation and reasoning for object tracking and scene interpretation in video , 2011, Expert Syst. Appl..

[21]  Christopher Bowman,et al.  Adaptive context discovery and exploitation , 2013, Proceedings of the 16th International Conference on Information Fusion.

[22]  José M. Molina López,et al.  Context-based multi-level information fusion for harbor surveillance , 2015, Inf. Fusion.