  In this paper we demonstrate that it is possible to manage intelligence in
constant time as a pre-process to information fusion through a series of
processes dealing with issues such as clustering reports, ranking reports with
respect to importance, extraction of prototypes from clusters and immediate
classification of newly arriving intelligence reports. These methods are used
when intelligence reports arrive which concerns different events which should
be handled independently, when it is not known a priori to which event each
intelligence report is related. We use clustering that runs as a back-end
process to partition the intelligence into subsets representing the events, and
in parallel, a fast classification that runs as a front-end process in order to
put the newly arriving intelligence into its correct information fusion
process.
