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Challenges in Automating theProvisioning of Parametric Initialization Data
to Simulation Applications
Briefing to the 20th ISMOR Symposium
Major Matthew Chesney
United States Army
Overview
Agenda
• Background – US Army’s Training and Doctrine Command Analysis Center (TRAC)
• Equipment Characteristics and Performance Data Interchange Format (DIF) – The Background project to the paper
• The Challenges and Recommendations – The Subject of our paper
TRAC-Monterey
DYNAMICSRESEARCHCORPORATION
Overview
Centers of Expertise
Ft Leavenworth Corps, Div, & Joint Operations
WSMR Bde & Bn Ops, Training, Costs
Ft Lee Logistics, Support & Sustain
Monterey Research
JFCOM Joint Experimentation
Fort Leavenworth
MontereyFort
Monroe
White Sands Missile Range
Fort LeeJFCOM
LEE Dr. Goodwin, Director LTC Wilson, Deputy
WSMR
DirectorTRAC
MTRY
Ms. Vargas, DirectorCOL Appleget, Deputy
Ass’t DCSSACOL Mitcham
LTC Cioppa, DirectorMr. Jackson, Deputy
Monroe
CGTRADOC
FLVN
Mr. Bauman, DirectorCOL Treharne, Deputy
Mr. Magee, DirectorCOL Lee, Deputy
JFCOM Ms. WinterMAJ Deller
TRAC Organization
Established1979
Overview
TRAC-Monterey Research Pillars
MOUT Modelingand Simulation
Advancements in simulation and OR
MethodologiesElements of
Combat Power
Overview
Advancements in Simulation and OR Methodologies
• Natural Decision-Making and Information Fusion- To represent how decisions are made for use in simulations
• Agent-Based Modeling- Determine potential for US Army; Application for DBBL
• Experimental Design- Most information from fewest number of runs
• JANUS vs. JCATS Attrition Algorithms- Comparison of Algorithms in Urban Environment
• Extensible M&S Framework- ‘Next Generation’ simulation architecture
• Characteristics and Performance Data Exchange Using XML- Reduce data manipulation requirements
• OneSAF / COMBATXXI Research Lab- Opportunities to leverage multiple efforts and organizations
• Acquisition Management Institute Initiative- Instantiate SMART in practice through focused research / education
Overview
Military•Army Modeling and Simulation Office•Army Aviation and Missile Defense Command•Army Aviation Center•Army Depth & Simultaneous Attack Battle Lab•Army Infantry Center•Army Simulation, Training, and Instrumentation Command•Air Force Training and Evaluation Command•Engineer Research and Development Center•Army Accessions Command•PM Soldier•TPO OneSAF•Army Material Systems Analysis Activity (AMSAA)•TRAC-FLVN•TRAC-WSMR
Contractors•Rolands and Associates, Inc.•Dynamics Research Corporation•Tapestry Solutions, Inc.•NovaLogic Systems•Wexford Group
AcademiaNPS:
•Computer Science•Engineering Management •Mathematics•Mechanical Engineering•Operations Analysis•Software Engineering•Systems Engineering
USMA•Systems EngineeringTRAC-Monterey
TRAC-Monterey Partnerships
Overview
Background
• State of the practice
• Data management
• Higher resolution model support
• Data interchange formats
• State of the practice
• Data management
• Higher resolution model support
• Data interchange formats
AR 5-11; “Management of Army Models and Simulations:
• Share valid data to all M&S data consumers
• Develop Standards to use common data
• Minimize Cost of Data
AR 5-11; “Management of Army Models and Simulations:
• Share valid data to all M&S data consumers
• Develop Standards to use common data
• Minimize Cost of Data
Overview
Current Data Exchange Methodology
Overview
Data Interchange Formats
Overview
Project Milestones
AnalyzeConsumer DataRequirements
Assess ProvidersFormats
Extend andDocument XMLData Standard
Data Requirements
FY01 Data Standard
Develop UserInterface
Demonstrate DIFUse
DemonstrationTool
Sample Producer Data Sample Consumer Data
XML Spy IDE
Manage ProjectFinal Report
DemoResults
FY02 Data Standard
Consumer DataRequirements
Provider Formats
ProducerFormats
ActivityInput
Control / Constraint
Output
Mechanism
Legend
Overview
C&P Data populated into
XML DIF Standard
OTB Import Routines
<?xml version=“1.0”?><EquipmentCPData> <tag>data</tag></EquipmentCPData>
NGIC Export Routines
AMSAA Export Routines
FY02 Providers/Consumers
DIMSRR SPIRIT Databases
OTB ReaderFiles
Authoritative Data Providers
Consuming Simulation Systems
XML Populated DIF(XPOD)
OOS Conversion Routines
Combat XXIImport Routines
Combat XXILegacyFormats
OOS KA/KERepository
Overview
Scope
The FY02 effort expands the scope to include:
• Indirect Fire systems,
• Communication systems,
• Sensor systems (i.e., Radar, IR, Day vision, and NVG systems), and
• Aircraft (i.e., fixed wing, rotary wing, and UAVs)
The FY01 scope was:• Ground systems and direct fire
Overview
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
Overview
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.
• 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.
Overview
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 DIF’s semantics.
• Proposed solution: decisions must be made regarding whether to delegate transformation requirements to the producer or the consumer.
• 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 DIF’s semantics.
• Proposed solution: decisions must be made regarding whether to delegate transformation requirements to the producer or the consumer.
Overview
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
• 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
<weight>18</weight>
<parameter>
<name>weight</name>
<value>18</value>
</parameter>
Overview
Standard Nomenclature
• Challenge: common naming is a significant, yet easily solved challenge in sharing simulation data. A variety of schemes are available
• Proposed solution: army’s 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
• Challenge: common naming is a significant, yet easily solved challenge in sharing simulation data. A variety of schemes are available
• Proposed solution: army’s 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
Overview
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
• Challenge: assignment of unique identifiers to simulation object types
• Proposed solution: Modernized Integrated Data Base (MIDB) over the IEEE Distributed Interactive Simulation Enumeration
Overview
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
• 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
Overview
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
• 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
Overview
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
• 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
Overview
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
• 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.”
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.”
Overview
Summary
• Semantic interoperability
• Semantic mapping responsibility
• Explicit tags vs. Meta-model approach,
• Standard nomenclature
• Entity type enumerations
• Versioning / Traceability
• Storage methods
• Distribution methods
• 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
Overview
Questions?