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heterogeneous and reusable components,



and high fidelity of detail in a scalable system.

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Sensitivity to Current Information Through Real-Time Knowledge Discovery
Continuously incorporating current information in SWS provides three key benefits that distinguish SWS
from other initiatives at constructing a simulated environment:


SWS remains up to date with respect to events and emerging trends.



SWS leverages the prodigious amounts of data from all publicly available data sources,
something that is infeasible for a small number of analysts to gather in a timely manner.



Models used by SWS are continuously refined, parameterized, and validated, keeping the
underlying model base of SWS relevant across time.

Burgeoning technology in the area of knowledge discovery has matured so that Web crawlers and spiders
are now used in research and industry. Applying this technology to news portals, blogs, and other internet
sources enables large amounts of data to be gathered and processed in a short amount of time.
By considering all available data, automated data mining provides an unbiased means of incorporating data
originating from multiple sources, and therefore, data from multiple perspectives. Additionally, interesting
outliers are discovered through text, video, and transaction analytics.
Believability and reliability metrics are applied to weight the influence of data from different sources
depending on the type of source, experience with data from the source, and the type of data. The
believability and reliability are then taken into account when incorporating the data into the SWS synthetic
world.
The discovery technology is coupled with a semantic engine that extracts semantics from the data. The
semantics are used to prepare the gathered data for use by the simulations and to relate the data to
knowledge already in the synthetic world.
Other sources of data besides the internet are incorporated using the same knowledge discover and
semantic extraction, such as proprietary, enterprise system, and classified data sources (classified and
proprietary data would only be incorporated in select excursions.)

The Ontological Representation of Knowledge
Using an ontological repository of knowledge, SWS augments analyst knowledge with simulation
semantics. The SWS ontological representation differs from traditional database approaches in that it stores
ontology as well as data. Ontology is a methodology for categorizing and annotating data based on logical,
human conceptualizations of what the data means. Interactions with the ontological annotations of data in
the repository are fundamentally different from the interactions allowed by the prevailing RDMS and
OODBMS approaches to database management. Traditional approaches coordinate tools with data by
identifying the physical data in queries whereas SWS enables coordination based on the semantic meaning
of data. A piece of data can be annotated with different keywords from diverse disciplines. Analysts as well
as automated processes can describe data needs using keywords from their own domains, enabling crossdisciplinary integration and a unified view of both real and synthetic information. A non-ambiguous data
categorization and annotation process is also needed to support the distributed and incremental process.
Data is also tagged with its informational sources and temporal lifetime. Consequently, multiple
experiments can be performed utilizing the same models combined with different points of views by
sourcing data from different informational sources or conducting an experiment in the context of a specific
calendar date.
A seminal integration of a knowledge repository with an active simulation environment was successfully
deployed in Urban Resolve 2015 (UR 2015). The implementation of an ontological representation of
knowledge, called eXtensible Net Assessment (xNA), was used to augment analyst knowledge with

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