each recognition system application. Instead, methods are needed to automatically generate the search trees and hypothesis organization strategies.

  1. Information integrity. Because data might be corrupted, faked, or inaccurate, not all information sources should be trusted equally. While technologies exist for authenticating information and securing its transfer, means of assessing confidence in information sources, and the ability to discard untrustworthy information, are topics that need further development.

  2. Information presentation. Information presentation, as opposed to representation, is the manner in which processed data is supplied to the human operator or commander. This involves the human-machine interface as well as the specific manner in which the data is displayed and its context established. Capabilities for data visualization and multimedia presentation of information will be important for the best performance of an information understanding system that necessarily includes a human operator as an integral subcomponent of the system.

  3. Human-performance prediction. An information understanding system that includes the human operator as the final arbitrator and decision-maker can be effective only if the human-machine interface is optimized with respect to human performance in the context of the task at hand. Accordingly, it will be necessary to acquire greater understanding of human cognition and decision-making behavior.

Because information understanding is a cross-cutting endeavor, other technology enablers in addition to those listed above will play a role in its realization. For example, networking technology, including data transfer and connectivity standards, will be an important factor.

SUMMARY

In the future, sources of information will include DOD organic systems, systems from other government agencies, emerging space-based and airborne commercial imaging systems, other commercial information providers, and public-domain sources that will emerge from the burgeoning information infrastructure.

As information becomes increasingly central to commerce, society will move from an environment of relative information scarcity to one of information abundance, in which applications must locate and correlate information from massive sets of seemingly unrelated data. Technology development is required to develop the applications that will effect the transformation of raw data to higher levels of information understanding, which will include not only the extraction of fixed patterns from sensor data, but also the analysis and reasoning about correlations and co-occurrence of relevant observations. Current applications typically perform pattern recognition on real-time data collected by dedicated sensors; future information understanding systems will need to perform higher-order reasoning about information from the full range of available information sources.



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement