Data Management Support to Tactical Data Fusion

Antony, Richard

doi:10.1201/9781420053098.ch24

Cited by 2 publications

(4 citation statements)

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“…Although the ABI analytic modeling approach mitigates many shortcomings, independent knowledge elicitation methods are still prone to human error (bias, anchoring, backfire effect, serial reasoning, time scale problems, etc.) (Antony, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Reporting from local governments, humanitarian and other NGOs, data from crisis mappers and activists, social media content from locals and refugees themselves, and imagery from handheld sources or commercial satellites can all inform our understanding of the situation and contribute to more accurate forecasting (Lewis et al , 2016). ABI supports situational awareness development and shorter decision-making timelines by improving methods of ingesting, managing, correlating, fusing, visualizing, and exploiting these wide varieties of data sources (Antony, 2016). We selected this critical need environment for our ABI analytic model elicitation and representation scheme evaluation.…”

Section: Abi Analytic Model Evaluation: Syrian Refugee and Internally Displaced Persons (Idps) Trend Analysismentioning

confidence: 99%

“…As a team, they go through a rigorous, time-intensive elicitation process to define a suitable modeling problem, curate and exploit data, cleanse and structure data, extract model features, model data in an appropriate program, and visualize and analyze model outcomes (Defense Advanced Research Projects Agency, 2016). With the global deficits of data scientists and methodologists, creation of analytic models must be simplified to fit within the current analyst workflow independent of data scientists and methodologists (Manyika et al , 2011), must be adaptable for participatory modeling (Pfaff et al , 2016) to enable collaboration and knowledge management, and must facilitate machine learning and automation (Antony, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…The key objective of machine learning and automation is to free analysts from tasks that can be rationally automated, but analysts must maintain some level of oversight of the automated system to validate its reliability and effectiveness (Antony, 2016). Prediction without justification is difficult to accept; in contrast, machine learning with justification loops presents data “rationales” to provide insight into the machine’s prediction (Lei et al , 2016).…”

Section: Introductionmentioning

confidence: 99%

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Simplifying humanitarian assistance/disaster relief analytic models using activity-based intelligence

Widener

Mazzuchi

Sarkani

2018

DPM

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Purpose The purpose of this paper is to propose an effective knowledge elicitation method and representation scheme that empowers humanitarian assistance/disaster relief (HA/DR) analysts and experts to create analytic models without the aid of data scientists and methodologists while addressing the issues of complexity, collaboration, and emerging technology across a diverse global network of HA/DR organizations. Design/methodology/approach The paper used a mixed-methods research approach, with qualitative research and analysis to select the model elicitation method, followed by quantitative data collection and evaluation to test the representation scheme. A simplified analytic modeling approach was created based on emerging activity-based intelligence (ABI) analytic methods. Findings Using open source data on the Syrian humanitarian crisis as the reference mission, ABI analytic models were proven capable in modeling HA/DR scenarios of physical systems, nonphysical systems, and thinking. Practical implications As a data-agnostic approach to develop object and network knowledge, ABI aligns with the objectives of modeling within multiple HA/DR organizations. Originality/value Using an analytic method as the basis for model creation allows for immediate adoption by analysts and removes the need for data scientists and methodologists in the elicitation phase. Applying this highly effective cross-domain ABI data fusion technique should also supplant the accuracy weaknesses created by traditional simplified analytic models.

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Section: Discussionmentioning

confidence: 99%

Section: Abi Analytic Model Evaluation: Syrian Refugee and Internally Displaced Persons (Idps) Trend Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simplifying humanitarian assistance/disaster relief analytic models using activity-based intelligence

Widener

Mazzuchi

Sarkani

2018

DPM

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Information Data Fusion and Computer Network Defense

Ballora

Giacobe

McNeese

et al. 2012

Situational Awareness in Computer Network Defense

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Computer networks no longer simply enable military and civilian operations, but have become vital infrastructures for all types of operations ranging from sensing and command/control to logistics, power distribution, and many other functions. Consequently, network attacks have become weapons of choice for adversaries engaged in asymmetric warfare. Traditionally, data and information fusion techniques were developed to improve situational awareness and threat assessment by combining data from diverse sources, and have recently been extended to include both physical (“hard”) sensors and human observers (acting as “soft” sensors). This chapter provides an introduction to traditional data fusion models and adapts them to the domain of cyber security. Recent advances in hard and soft information fusion are summarized and applied to the cyber security domain. Research on the use of sound for human-in-the-loop pattern recognition (sonification) is also introduced. Finally, perspectives are provided on the future for data fusion in cyber security research.

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Data Management Support to Tactical Data Fusion

Cited by 2 publications

References 0 publications

Simplifying humanitarian assistance/disaster relief analytic models using activity-based intelligence

Simplifying humanitarian assistance/disaster relief analytic models using activity-based intelligence

Information Data Fusion and Computer Network Defense

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