Managing Inconsistent Intelligence

TL;DR

This paper presents a real-time pre-processing framework for managing incoming intelligence reports by clustering and classifying them efficiently, enabling independent handling of different events without prior knowledge.

Contribution

It introduces a novel approach combining clustering and fast classification to manage diverse intelligence reports in constant time, improving information fusion processes.

Findings

Efficient clustering partitions reports into event-specific subsets.

Fast classification accurately assigns new reports to existing clusters.

Pre-processing enables independent event handling in real-time.

Abstract

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.