CoRAVEN: modeling and design of a multimedia intelligent infrastructure for collaborative intelligence analysis

Jones, Patricia M.; Hayes, Caroline C.; Wilkins, Charles A.; Bargar, Robin; Sniezek, Janet A.; Asaro, Peter; Mengshoel, Ole J.; Keßler, Dustin; Lucenti, M.; Choi, In-Hwa; Tu, Nguyen Ba; Schlabach, M.J.

doi:10.1109/icsmc.1998.725532

Cited by 23 publications

(14 citation statements)

References 4 publications

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“…The Munin BNs are from the medical field of electromyography. The Pir BNs perform information filtering for the purpose of improving battlefield situation awareness [44]. The Water BN models the biological processes of water purification.…”

Section: Experimental Methodologymentioning

confidence: 99%

Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks

Mengshoel

Wilkins²,

Roth

2011

IEEE Trans. Knowl. Data Eng.

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Abstract-For hard computational problems, stochastic local search has proven to be a competitive approach to finding optimal or approximately optimal problem solutions. Two key research questions for stochastic local search algorithms are: Which algorithms are effective for initialization? When should the search process be restarted? In the present work, we investigate these research questions in the context of approximate computation of most probable explanations (MPEs) in Bayesian networks (BNs). We introduce a novel approach, based on the Viterbi algorithm, to explanation initialization in BNs. While the Viterbi algorithm works on sequences and trees, our approach works on BNs with arbitrary topologies. We also give a novel formalization of stochastic local search, with focus on initialization and restart, using probability theory and mixture models. Experimentally, we apply our methods to the problem of MPE computation, using a stochastic local search algorithm known as Stochastic Greedy Search. By carefully optimizing both initialization and restart, we reduce the MPE search time for application BNs by several orders of magnitude compared to using uniform at random initialization without restart. On several BNs from applications, the performance of Stochastic Greedy Search is competitive with clique tree clustering, a state-of-the-art exact algorithm used for MPE computation in BNs.

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Section: Experimental Methodologymentioning

confidence: 99%

Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks

Mengshoel

Wilkins²,

Roth

2011

IEEE Trans. Knowl. Data Eng.

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“…style of COA analysis. These two systems are designed to be used in conjunction with the CoRaven (or perhaps other modeling or forecasting tools) system for intelligence analysis (Jones, et al, 1998). Together, these individual DSSs can constitute a more complete decision support framework for mitigation.…”

Section: Decision Support Systemsmentioning

confidence: 99%

Sociocultural Behavior Sensemaking: State of the Art in Understanding the Operational Environment

Egeth¹,

Klein²,

Schmorrow³

2014

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“…In addition to being well-suited to automated analysis, these graphical models are also amenable to visualization 1 . While most BN inference problems are computationally hard in the general case, efficient algorithms have been developed [6,17] that open the path toward successful applications in a wide range of automated reasoning areas, for example in model-based diagnosis (e.g., [21]), sensor validation [2,22], or intelligent data analysis [15,27]. …”

Section: Advanced Iswhm Modelingmentioning

confidence: 99%

Towards software health management with bayesian networks

Schumann

Mengshoel

Srivastava

et al. 2010

Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research

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More and more systems (e.g., aircraft, machinery, cars) rely heavily on software, which performs safety-critical operations. Assuring software safety though traditional V&V has become a tremendous, if not impossible task, given the growing size and complexity of the software.We propose that iSWHM (Integrated SoftWare Health Management) can increase safety and reliability of highassurance software systems. iSWHM uses advanced techniques from the area of system health management in order to continuously monitor the behavior of the software during operation, quickly detect anomalies and perform automatic and reliable root-cause analysis, while not replacing traditional V&V. Information provided by the iSWHM system can be used for automatic mitigation mechanisms (e.g., recovery, dynamic reconfiguration) or presented to a human operator. iSWHM's prognostic capabilities will further improve reliability and availability as it provides information about soon-to-occur failures or looming performance bottlenecks. In this paper, we will discuss challenges and future potential and describe how Bayesian networks (BN) could be used for iSWHM modeling.

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CoRAVEN: modeling and design of a multimedia intelligent infrastructure for collaborative intelligence analysis

Cited by 23 publications

References 4 publications

Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks

Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks

Sociocultural Behavior Sensemaking: State of the Art in Understanding the Operational Environment

Towards software health management with bayesian networks

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