A Context-Driven Framework for Proactive Decision Support With Applications

Major challenges anticipated in the future C<sup>4</sup>ISR (command, control, communications, computers, intelligence, surveillance, and reconnaissance) operations involve rapid mission planning/ re-planning in highly dynamic, asymmetric, unpredictable, and network-centric environments....

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Main Authors: Manisha Mishra, David Sidoti, Gopi Vinod Avvari, Pujitha Mannaru, Diego Fernando Martinez Ayala, Krishna R. Pattipati, David L. Kleinman
Format: Article
Language:English
Published: IEEE 2017-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/7932848/
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spelling doaj-91d98ad7b43d4c7b9a20685cc0977c7d2021-03-29T20:17:15ZengIEEEIEEE Access2169-35362017-01-015124751249510.1109/ACCESS.2017.27070917932848A Context-Driven Framework for Proactive Decision Support With ApplicationsManisha Mishra0https://orcid.org/0000-0002-3427-0477David Sidoti1Gopi Vinod Avvari2Pujitha Mannaru3Diego Fernando Martinez Ayala4Krishna R. Pattipati5David L. Kleinman6Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USADepartment of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USADepartment of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USADepartment of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USADepartment of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USADepartment of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USANaval Postgraduate School, Monterey, CA, USAMajor challenges anticipated in the future C<sup>4</sup>ISR (command, control, communications, computers, intelligence, surveillance, and reconnaissance) operations involve rapid mission planning/ re-planning in highly dynamic, asymmetric, unpredictable, and network-centric environments. Developing decision support for such complex mission environments requires automated processing, interpretation, and development of proactive decisions using large volumes of structured, unstructured, and semi-structured data, while simultaneously decreasing the time necessary to arrive at a decision. To overcome this data deluge, there is a need for mastering information dominance via acquisition, fusion, and transfer of the right data/information/knowledge from the right sources in the right mission context to the right decision-maker (DM) at the right time for the right purpose (6R). The fundamental challenge in achieving the 6R is to conceive a generic framework that encompasses the dynamics of relevant contextual elements, their interdependence and correlation to the current and evolving situation, while taking into account the cognitive status of the DM. In this paper, we propose a context-driven proactive decision support (PDS) framework that comprises: 1) adaptive model-based dynamic graph models (e.g., Dynamic Hierarchical Bayesian Networks) and the concomitant inference algorithms for context representation, inference, and forecasting, 2) information selection, valuation, and prioritization methods for context-driven operations, including uncertainty management approaches, and 3) application of PDS concepts for courses of action recommendations across representative maritime operations.https://ieeexplore.ieee.org/document/7932848/Context-aware decision supportcontext representationuncertainty managementproactive decision support
collection DOAJ
language English
format Article
sources DOAJ
author Manisha Mishra
David Sidoti
Gopi Vinod Avvari
Pujitha Mannaru
Diego Fernando Martinez Ayala
Krishna R. Pattipati
David L. Kleinman
spellingShingle Manisha Mishra
David Sidoti
Gopi Vinod Avvari
Pujitha Mannaru
Diego Fernando Martinez Ayala
Krishna R. Pattipati
David L. Kleinman
A Context-Driven Framework for Proactive Decision Support With Applications
IEEE Access
Context-aware decision support
context representation
uncertainty management
proactive decision support
author_facet Manisha Mishra
David Sidoti
Gopi Vinod Avvari
Pujitha Mannaru
Diego Fernando Martinez Ayala
Krishna R. Pattipati
David L. Kleinman
author_sort Manisha Mishra
title A Context-Driven Framework for Proactive Decision Support With Applications
title_short A Context-Driven Framework for Proactive Decision Support With Applications
title_full A Context-Driven Framework for Proactive Decision Support With Applications
title_fullStr A Context-Driven Framework for Proactive Decision Support With Applications
title_full_unstemmed A Context-Driven Framework for Proactive Decision Support With Applications
title_sort context-driven framework for proactive decision support with applications
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2017-01-01
description Major challenges anticipated in the future C<sup>4</sup>ISR (command, control, communications, computers, intelligence, surveillance, and reconnaissance) operations involve rapid mission planning/ re-planning in highly dynamic, asymmetric, unpredictable, and network-centric environments. Developing decision support for such complex mission environments requires automated processing, interpretation, and development of proactive decisions using large volumes of structured, unstructured, and semi-structured data, while simultaneously decreasing the time necessary to arrive at a decision. To overcome this data deluge, there is a need for mastering information dominance via acquisition, fusion, and transfer of the right data/information/knowledge from the right sources in the right mission context to the right decision-maker (DM) at the right time for the right purpose (6R). The fundamental challenge in achieving the 6R is to conceive a generic framework that encompasses the dynamics of relevant contextual elements, their interdependence and correlation to the current and evolving situation, while taking into account the cognitive status of the DM. In this paper, we propose a context-driven proactive decision support (PDS) framework that comprises: 1) adaptive model-based dynamic graph models (e.g., Dynamic Hierarchical Bayesian Networks) and the concomitant inference algorithms for context representation, inference, and forecasting, 2) information selection, valuation, and prioritization methods for context-driven operations, including uncertainty management approaches, and 3) application of PDS concepts for courses of action recommendations across representative maritime operations.
topic Context-aware decision support
context representation
uncertainty management
proactive decision support
url https://ieeexplore.ieee.org/document/7932848/
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