Finding decision support requirements for effective intelligence analysis tools

2005 ◽  
Author(s):  
William Elm ◽  
Scott Potter ◽  
James Tittle ◽  
David Woods ◽  
Justin Grossman ◽  
...  
Keyword(s):  
Decision Support ◽  
Intelligence Analysis ◽  
Analysis Tools

Finding Decision Support Requirements for Effective Intelligence Analysis Tools

2005 ◽  
Vol 49 (3) ◽  
pp. 297-301 ◽  
Author(s):  
William Elm ◽  
Scott Potter ◽  
James Tittle ◽  
David Woods ◽  
Justin Grossman ◽  
...  
Keyword(s):  
Decision Support ◽  
Systems Engineering ◽  
Support Function ◽  
Cognitive Task ◽  
Intelligence Community ◽  
Intelligence Analysis ◽  
Large Set ◽  
Cognitive Systems Engineering ◽  
Support Functions ◽  
Analysis Tools

Within ARDA's GI2Vis program, we developed a unique framework for the definition of decision support requirements for intelligence analysis tools. This framework, based on a first-of-a-kind integration of a model of inferential analysis and principles for designing effective human-computer teams from Cognitive Systems Engineering, has defined the essential support functions to be provided to the intelligence analyst(s). This model has proven to be extremely useful in assessing the support provided by a large set of visualization tools. This assessment has identified clusters of support functions that are addressed by many tools as well as key missing support functions. In this way, the Support Function Model has been used to identify gaps in the support function coverage of existing tools. This can serve as a valuable focusing mechanism for future design and development efforts. In addition, we believe this would be a useful mechanism to enhance cross-discussions among research teams involved in Cognitive Task Analysis efforts within the Intelligence Community. Having others integrate their analytic results with this framework would provide the mechanism for expansion of this model to become a more robust tool and have an even greater impact on the Intelligence Community.

Overview of Movement Analysis and Gait Features

2011 ◽  
pp. 1-69 ◽  
Author(s):  
Russell Best ◽  
Rezaul Begg
Keyword(s):  
Computational Intelligence ◽  
Movement Analysis ◽  
Research Process ◽  
Machine Learning Techniques ◽  
Intelligence Analysis ◽  
Objective Data ◽  
Analysis Tools ◽  
Learning Techniques ◽  
Gait Features ◽  
Key Features

This chapter provides an overview of the commonly used motion analysis approaches and techniques and the key features that are extracted from movement patterns for characterizing gait. The ultimate goal of gait analysis should be to provide reliable, objective data on which to base clinical decisions (Kaufman, 1998). Thousands of gait features/parameters have been used over the years. Selection of the correct gait features forms an important part of the research process, and often the success of the research outcomes depends heavily on selecting the most appropriate gait features. Analysis tools based on both statistical and machine-learning techniques use various types of gait features, ranging from the basic and directly measurable parameters to parameters that have undergone significant data processing and treatments. In this chapter, we attempt to introduce the commonly used methods to extract these features for use with the various statistical and computational intelligence analysis tools.

Irradiance co-spectrum analysis: Tools for decision support and technological planning

Solar Energy ◽  
2013 ◽  
Vol 95 ◽  
pp. 364-375 ◽  
Author(s):  
Jeff Rayl ◽  
George S. Young ◽  
Jeffrey R.S. Brownson
Keyword(s):  
Decision Support ◽  
Spectrum Analysis ◽  
Analysis Tools ◽  
Technological Planning

Clinical decision support

2013 ◽  
Vol 46 (2) ◽  
pp. 52
Author(s):  
CHRISTOPHER NOTTE ◽  
NEIL SKOLNIK
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision
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