Tuesday, April 23, 2013

Provenance of Intelligence Analysis Using Visual Analytics

Intelligence agencies often retrieve information from multiple sources to anticipate and counter terrorist attacks. Often difficulties of organizing this information arise when dealing with large quantities of dynamically changing information. Interactive visuals tools allow for effectively analyzing data and providing faster insight. Post Cold War centralized terrorist organizations have been replaced by decentralized terrorist cells that continually reinvent threats. In this case, visual analytics in intelligence is extremely useful for identifying behaviors of terrorist groups. For example, some of the advantages of using visual tools to study behaviors of a network of individuals include identifying key players of the complex network, identify emerging themes, indicate geo-spatial relationships, and show correlations across multiple parameters.  

During intelligence analysis, raw information processed through multiple stages is transformed into actionable intelligence. According to the authors, processing a large amount of raw material requires the use of visual tools to track the transformation. Both evolving and complete analysis should involve a review of the analytic process including tracking the sources of data and their reliability, reviewing background knowledge of the scenario in question, and analysts assumptions. Also known as provenance information, information that is traceable is valuable when assessing the plausibility of conclusions. Also a framework of permission management can be applied to visual tools in an effort to manage transparency among analysis with differing security clearances. The authors’ approach to traceability includes three categories; data level, analysis products, and reasoning products. The framework is based on products present in any analytic workflow. However, a number of challenges exist in tracing provenance during intelligence analysis. The authors suggest another framework called provenance reasoning workspace that comprise of three spaces to reduce these challenges. The spaces include a data space, a computation space, and a reasoning space.                              

Although this article was short, it was not an easy read. The article had a few sentence fragments and run on sentences. I had to read some sentences a number of times to understand the content. The authors focus was on using visual tools to keep track of the process of collecting information to analyzing intelligence as oppose to using it as a tool that enhances the presentation of the final product to the decision makers. Most of the article focused on providing advantages of using visual tools to trace the transformation of information to intelligence followed by frameworks. The information on the article needed to be explained in more than two pages. The authors’ framework for tracing the transformation of information into intelligence is not very descriptive. An example or a methodology section testing the frameworks may have shown the applicability of the frameworks.   

This reminds me of the article that I summarized about decision trees. Decision tress may not trace the process of gathering and analyzing intelligence, but it is a tool that can visually organize data for the analyst’s use rather than the decision maker’s. The authors’ frameworks are prone to similar challenges of incorporating decision trees to intelligence process. Similar to decision trees and contrary to the authors’ view, the provenance frameworks may not be able to handle complex scenarios. It seems quite tedious and requires the use of two different frameworks. Organizing data at each phase will require the use of multiple tools. It also requires the analysts to update each step when incorporating new evidence. I’m unsure of the benefit of tracing the process of creating intelligence from the beginning to the end in a visual manner.
Wong, W., Xu, K., & Attfield, S. (n.d.). Provenance for intelligence analysis using visual analytics. 
         Retrieved from http://eprints.mdx.ac.uk/8415/1/intelligence-analysis-provenance1.pdf


  1. I disagree with the statement made by the authors claiming processing a large amount of raw material requires the use of visual tools to track the transformation. While visual tools may help not only the analyst but the decision maker see the transformation more clearly, it can still be done using traditional methods. Furthermore, I think an ideal situation would incorporate both visual tools as well as more traditional methods.

    Additionally, I think the suggestions made in the article are valid. Considering its short length an extension of the article may be useful in validating the findings of the authors.

  2. Our topics are fairly similar with both authors wanting to show how visual analytics applies to the intelligence field. I agree that this article could have been longer in order to explain things more effectively. Additionally, where my article kind of helps your article is the fact that having traceable evidence means that someone else can pick up your project where you left off and have a good grasp of how to proceed. What I think they were trying to get at as well is that the products produced should be able to change into different forms.