A visual analytic framework for data fusion in investigative intelligence

Guoray Cai, Geoff Gross, James Llinas, David Hall

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Intelligence analysis depends on data fusion systems to provide capabilities of detecting and tracking important objects, events, and their relationships in connection to an analytical situation. However, automated data fusion technologies are not mature enough to offer reliable and trustworthy information for situation awareness. Given the trend of increasing sophistication of data fusion algorithms and loss of transparency in data fusion process, analysts are left out of the data fusion process cycle with little to no control and confidence on the data fusion outcome. Following the recent rethinking of data fusion as human-centered process, this paper proposes a conceptual framework towards developing alternative data fusion architecture. This idea is inspired by the recent advances in our understanding of human cognitive systems, the science of visual analytics, and the latest thinking about human-centered data fusion. Our conceptual framework is supported by an analysis of the limitation of existing fully automated data fusion systems where the effectiveness of important algorithmic decisions depend on the availability of expert knowledge or the knowledge of the analyst's mental state in an investigation. The success of this effort will result in next generation data fusion systems that can be better trusted while maintaining high throughput.

Original languageEnglish (US)
Title of host publicationNext-Generation Analyst II
PublisherSPIE
Volume9122
ISBN (Print)9781628410594
DOIs
StatePublished - Jan 1 2014
EventNext-Generation Analyst II - Baltimore, MD, United States
Duration: May 6 2014May 6 2014

Other

OtherNext-Generation Analyst II
CountryUnited States
CityBaltimore, MD
Period5/6/145/6/14

Fingerprint

Visual Analytics
intelligence
multisensor fusion
Data Fusion
Data fusion
Framework
Intelligence
Cognitive systems
Cognitive Systems
Situation Awareness
Object Tracking
Transparency
High Throughput
Confidence
availability
confidence
Availability

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Cai, G., Gross, G., Llinas, J., & Hall, D. (2014). A visual analytic framework for data fusion in investigative intelligence. In Next-Generation Analyst II (Vol. 9122). [91220A] SPIE. https://doi.org/10.1117/12.2053161
Cai, Guoray ; Gross, Geoff ; Llinas, James ; Hall, David. / A visual analytic framework for data fusion in investigative intelligence. Next-Generation Analyst II. Vol. 9122 SPIE, 2014.
@inproceedings{549579c01c3249bc8ab36f0103a40d66,
title = "A visual analytic framework for data fusion in investigative intelligence",
abstract = "Intelligence analysis depends on data fusion systems to provide capabilities of detecting and tracking important objects, events, and their relationships in connection to an analytical situation. However, automated data fusion technologies are not mature enough to offer reliable and trustworthy information for situation awareness. Given the trend of increasing sophistication of data fusion algorithms and loss of transparency in data fusion process, analysts are left out of the data fusion process cycle with little to no control and confidence on the data fusion outcome. Following the recent rethinking of data fusion as human-centered process, this paper proposes a conceptual framework towards developing alternative data fusion architecture. This idea is inspired by the recent advances in our understanding of human cognitive systems, the science of visual analytics, and the latest thinking about human-centered data fusion. Our conceptual framework is supported by an analysis of the limitation of existing fully automated data fusion systems where the effectiveness of important algorithmic decisions depend on the availability of expert knowledge or the knowledge of the analyst's mental state in an investigation. The success of this effort will result in next generation data fusion systems that can be better trusted while maintaining high throughput.",
author = "Guoray Cai and Geoff Gross and James Llinas and David Hall",
year = "2014",
month = "1",
day = "1",
doi = "10.1117/12.2053161",
language = "English (US)",
isbn = "9781628410594",
volume = "9122",
booktitle = "Next-Generation Analyst II",
publisher = "SPIE",
address = "United States",

}

Cai, G, Gross, G, Llinas, J & Hall, D 2014, A visual analytic framework for data fusion in investigative intelligence. in Next-Generation Analyst II. vol. 9122, 91220A, SPIE, Next-Generation Analyst II, Baltimore, MD, United States, 5/6/14. https://doi.org/10.1117/12.2053161

A visual analytic framework for data fusion in investigative intelligence. / Cai, Guoray; Gross, Geoff; Llinas, James; Hall, David.

Next-Generation Analyst II. Vol. 9122 SPIE, 2014. 91220A.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - A visual analytic framework for data fusion in investigative intelligence

AU - Cai, Guoray

AU - Gross, Geoff

AU - Llinas, James

AU - Hall, David

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Intelligence analysis depends on data fusion systems to provide capabilities of detecting and tracking important objects, events, and their relationships in connection to an analytical situation. However, automated data fusion technologies are not mature enough to offer reliable and trustworthy information for situation awareness. Given the trend of increasing sophistication of data fusion algorithms and loss of transparency in data fusion process, analysts are left out of the data fusion process cycle with little to no control and confidence on the data fusion outcome. Following the recent rethinking of data fusion as human-centered process, this paper proposes a conceptual framework towards developing alternative data fusion architecture. This idea is inspired by the recent advances in our understanding of human cognitive systems, the science of visual analytics, and the latest thinking about human-centered data fusion. Our conceptual framework is supported by an analysis of the limitation of existing fully automated data fusion systems where the effectiveness of important algorithmic decisions depend on the availability of expert knowledge or the knowledge of the analyst's mental state in an investigation. The success of this effort will result in next generation data fusion systems that can be better trusted while maintaining high throughput.

AB - Intelligence analysis depends on data fusion systems to provide capabilities of detecting and tracking important objects, events, and their relationships in connection to an analytical situation. However, automated data fusion technologies are not mature enough to offer reliable and trustworthy information for situation awareness. Given the trend of increasing sophistication of data fusion algorithms and loss of transparency in data fusion process, analysts are left out of the data fusion process cycle with little to no control and confidence on the data fusion outcome. Following the recent rethinking of data fusion as human-centered process, this paper proposes a conceptual framework towards developing alternative data fusion architecture. This idea is inspired by the recent advances in our understanding of human cognitive systems, the science of visual analytics, and the latest thinking about human-centered data fusion. Our conceptual framework is supported by an analysis of the limitation of existing fully automated data fusion systems where the effectiveness of important algorithmic decisions depend on the availability of expert knowledge or the knowledge of the analyst's mental state in an investigation. The success of this effort will result in next generation data fusion systems that can be better trusted while maintaining high throughput.

UR - http://www.scopus.com/inward/record.url?scp=84906329951&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84906329951&partnerID=8YFLogxK

U2 - 10.1117/12.2053161

DO - 10.1117/12.2053161

M3 - Conference contribution

AN - SCOPUS:84906329951

SN - 9781628410594

VL - 9122

BT - Next-Generation Analyst II

PB - SPIE

ER -

Cai G, Gross G, Llinas J, Hall D. A visual analytic framework for data fusion in investigative intelligence. In Next-Generation Analyst II. Vol. 9122. SPIE. 2014. 91220A https://doi.org/10.1117/12.2053161