Situation Management in Crisis Scenarios Based on Self-Organizing Neural Mapping Technology

@inproceedings{TangoLowy05, abstract = {During crisis situations decision makers are severely limited in their ability to consider system-wide conditions, predict changes in conditions, and access relevant expert knowledge and opinions. Useful information and knowledge are likely to be highly unstructured and widely dispersed. In this paper we describe an approach and accompanying algorithms that enable relationships to be developed and conclusions to be drawn from unstructured data obtained from disparate sources and stakeholders. The approach transforms ars Cognita's existing Self-Organizing Neural Mapping technology from a passive knowledge acquisition and delivery system to an active system that can predict outcomes based upon the relationships inherent in its collected knowledge.}, author = {Tango-Lowy, R. and Lewis, L. }, booktitle = {Military Communications Conference, 2005. MILCOM 2005. IEEE}, citeulike-article-id = {3036903}, doi = {http://dx.doi.org/10.1109/MILCOM.2005.1605913}, journal = {Military Communications Conference, 2005. MILCOM 2005. IEEE}, keywords = {cas\_chapter, military, security, som}, pages = {1--7}, posted-at = {2008-07-23 14:46:52}, priority = {4}, title = {Situation Management in Crisis Scenarios Based on Self-Organizing Neural Mapping Technology}, url = {http://dx.doi.org/10.1109/MILCOM.2005.1605913}, year = {2005} }

TY - CONF ID - TangoLowy05 L3 - citeulike-article-id:3036903 TI - Situation Management in Crisis Scenarios Based on Self-Organizing Neural Mapping Technology T2 - Military Communications Conference, 2005. MILCOM 2005. IEEE JF - Military Communications Conference, 2005. MILCOM 2005. IEEE SP - 1 EP - 7 N2 - During crisis situations decision makers are severely limited in their ability to consider system-wide conditions, predict changes in conditions, and access relevant expert knowledge and opinions. Useful information and knowledge are likely to be highly unstructured and widely dispersed. In this paper we describe an approach and accompanying algorithms that enable relationships to be developed and conclusions to be drawn from unstructured data obtained from disparate sources and stakeholders. The approach transforms ars Cognita's existing Self-Organizing Neural Mapping technology from a passive knowledge acquisition and delivery system to an active system that can predict outcomes based upon the relationships inherent in its collected knowledge. KW - cas_chapter KW - military KW - security KW - som AU - Tango-Lowy, R AU - Lewis, L PY - 2005/// UR - http://dx.doi.org/10.1109/MILCOM.2005.1605913 ER -