Challenges in Automating the

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Challenges in Automating the Provisioning of
Parametric Initialization Data to Simulation
Applications
Lee Lacy, Mike Puhlmann, Gabriel Aviles, Wayne
Randolph - Dynamics Research Corporation Jin
Kwon AMSAA Major Matt Chesney - U.S. Army
TRAC-Monterey
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Background

• AR 5-11 Management of Army Models and
  Simulations
• Share valid data to all MS data consumers
• Develop Standards to use common data
• Minimize Cost of Data

• Higher resolution model support
• Data management
• State of the practice
• Data interchange formats

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DIF Implementation Challenges and Potential
Solutions

• Semantic Interoperability,
• Semantic Mapping Responsibility,
• Explicit Tags vs. Meta-model Approach,
• Standard Nomenclature,
• Entity Type Enumerations,
• Versioning / Traceability,
• Storage Methods,
• Distribution Methods, and
• Standards Development Process

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Semantic Interoperability

• Challenge although XML can help solve syntactic
  interoperability challenges, differences in
  producer and consumer semantics (the meaning of
  the data) must be addressed in other ways.
• Proposed solution standardizing on data models
  and providing explicit semantics.

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Semantic Mapping Responsibility

• Challenge although a DIF provides a standard
  data format, both producers and consumers will
  likely require a mapping process to translate
  their data to/from their data models into the
  DIFs semantics.
• Proposed solution decisions must be made
  regarding whether to delegate transformation
  requirements to the producer or the consumer.

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Explicit Tags vs. Meta-model Approach

• Challenge
• Explicit DIFs use tag names that contain the
  identifier of the data value being passed
• The meta-model approach involves using a single
  tag name and passing the data value identifiers
  as text string data
• Proposed Solution Compromise embed explicit
  tags only when necessary in a Meta Model

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Standard Nomenclature

• Challenge common naming is a significant, yet
  easily solved challenge in sharing simulation
  data. A variety of schemes are available
• Proposed solution armys standard nomenclature
  database (SND) for equipment and munition naming
  used by army analytical community in support of
  army studies and the DMSO common semantics and
  syntax effort for other parametric descriptions

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Entity Type Enumerations

• Challenge assignment of unique identifiers to
  simulation object types
• Proposed solution Modernized Integrated Data
  Base (MIDB) over the IEEE Distributed Interactive
  Simulation Enumeration

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Versioning / Traceability

• Challenge pedigree or provenance of the data is
  especially important to verification and
  validation agents
• Proposed Solution provide metadata with the data
  that indicates the version and pedigree of the
  data. The metadata may be needed down to the
  individual data items level

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Storage Methods

• Challenge large file sizes and classification
  levels
• Proposed solution Physical storage not a huge
  limiter but using short tag names, using tabs
  instead of spaces, limiting embedded comments,
  using explicit DIFs, and subdividing documents
  will help

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Distribution Methods

• Challenge distributing data
• Proposed solution
• Immediate solution is to use web portals that use
  human intervention.
• The future vision is to enable web services that
  automatically respond to consuming software
  requests for data. The goal should be to
  decrease the amount of human intervention.
  Included services should be able to check the
  version of data and update the data where needed

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Standardization Process

• Challenge standards Interoperability ontology
  and other agreements must be developed in a
  collaborative environment with input from various
  interests and compromises on the approach.
• Proposed solution SISO and other industry
  groups develop and document standards. Develop
  Recommended Practice Document

As defined by AR 5-11, Data Standards is A
capability that increases information sharing
effectiveness by establishing standardization of
data elements, data base construction,
accessibility procedures, system communication,
data maintenance and control.
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Summary

• Semantic interoperability
• Semantic mapping responsibility
• Explicit tags vs. Meta-model approach,
• Standard nomenclature,
• Entity type enumerations,
• Versioning / Traceability,
• Storage methods,
• Distribution methods, and
• Standards development process

• Provide explicit semantics
• Decision Consumer or Producer
• Compromise combined data model
• SND and DMSO common semantics
• MIDB before IEEE
• Provide archive info meta data
• Limit tag names, spaces and comments
• Web Services
• SISO

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Backup Slides
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Providing CP Data with a DIF
Export to common XML format
Import to simulations formats
Data Consumers (e.g., OneSAF)
XML DIF
Authoritative Data Sources in Multiple Formats
Simulation System Data in Multiple Formats
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Data Mapping
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Summary

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Questions?