MCDS: A Distributed Multi-organizational Collaborative Decision Support System for Emergency Preparedness
Department of Defense
Agency Tracking Number:
Solicitation Topic Code:
Small Business Information
PO Box 2145, Columbia, MD, 21045
Socially and Economically Disadvantaged:
Chief Operating Officer
Chief Operating Officer
AbstractThis proposal suggests the design and development of a kernel for distributed collaborative multi-organizational decision support system for emergency preparedness, anti-terrorism, and homeland defense. The proposed research is based on a systematicapproach that pays careful attention to both technical and human factors in the process. It offers a collection of novel technology-based solutions to efficiently manage, query, and mine the distributed data sources using some emerging tools likeDAML-based representation of data. It incorporates psychological/social/organizational models, provides graphical interfaces for creating domain ontology, distributed and privacy-preserving data mining techniques, interfaces for mobile devices developedusing advanced HCI techniques, and dynamic resource discovery for onsite emergency response team. Agnik's team for performing this research has stellar background and long experience in related areas. The PI has extensive experience in systems development.Professor Kargupta is a highly recognized figure in the area of distributed data mining and data stream mining. Professor Joshi is an expert in the area of Semantic Web, DAML, and mobile systems. Professor Sears is highly acclaimed for his work on humancomputer interfaces for mobile and desktop systems. Professor Provine is a behavioral psychologists and he has written numerous articles on terrorism. The proposed solution has direct commercial potential in the area of corporateenvironment that has lots of heterogeneous distributed data that are uneconomicalto move to a central location. Also, this application has immense advantage ofpreserving privacy by not moving the data. This is particularly advantageouswhen the data is classified or sensitive. The mobile part of the application willbe very useful for businesses that deal with time-critical data and collect thatthrough multiple customers touch points.Other than the above-mentioned areas the proposed system will be applicable tohandle other decision-support systems that handle emergencies other than anterrorist attack, e.g., flooding. As the system will be dynamic and user-friendly,new models can be entered easily to make the system adaptive for other applicationsas well.
* information listed above is at the time of submission.