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Analysts are faced with mountains of data, and finding that relevant piece of information is the proverbial needle in a
haystack, only with dozens of haystacks. Analysis tools that facilitate identifying causal relationships across multiple
data sets are sorely needed. 21st Century Systems, Inc. (21CSi) has initiated research called Causal-View, a causal datamining
visualization tool, to address this challenge. Causal-View is built on an agent-enabled framework. Much of the
processing that Causal-View will do is in the background. When a user requests information, Data Extraction Agents
launch to gather information. This initial search is a raw, Monte Carlo type search designed to gather everything
available that may have relevance to an individual, location, associations, and more. This data is then processed by Data-
Mining Agents. The Data-Mining Agents are driven by user supplied feature parameters. If the analyst is looking to see
if the individual frequents a known haven for insurgents he may request information on his last known locations. Or, if
the analyst is trying to see if there is a pattern in the individual's contacts, the mining agent can be instructed with the
type and relevance of the information fields to look at. The same data is extracted from the database, but the Data
Mining Agents customize the feature set to determine causal relationships the user is interested in. At this point, a
Hypothesis Generation and Data Reasoning Agents take over to form conditional hypotheses about the data and pare the
data, respectively. The newly formed information is then published to the agent communication backbone of Causal-
View to be displayed. Causal-View provides causal analysis tools to fill the gaps in the causal chain. We present here the
Causal-View concept, the initial research into data mining tools that assist in forming the causal relationships, and our
initial findings.
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