Challenges in Automating the
Provisioning of Parametric Initialization Data
to Simulation Applications
Briefing to the 20th ISMOR Symposium
Major Matthew Chesney
United States Army
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
DYNAMICS
RESEARCH
CORPORATION
TRAC-Monterey
Overview
TRAC Organization
CG
TRADOC
Ass’t DCSSA
Monroe COL Mitcham
Director
TRAC Mr. Bauman, Director
COL Treharne, Deputy
FLVN
Established
1979
Monterey
WSMR
LEE
Dr. Goodwin, Director
LTC Wilson, Deputy
JFCOM
LTC Cioppa, Director
Mr. Jackson, Deputy
Ms. Winter
MAJ Deller
Fort
Monroe
Fort Lee
Mr. Magee, Director
COL Lee, Deputy
Ms. Vargas, Director
COL Appleget, Deputy
MTRY
Fort
Leavenworth
JFCOM
White Sands
Missile Range
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
Overview
TRAC-Monterey Research Pillars
MOUT Modeling
and Simulation
Advancements in
simulation and OR
Methodologies
Elements 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
TRAC-Monterey Partnerships
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
Academia
NPS:
•Computer Science
•Engineering Management
•Mathematics
•Mechanical Engineering
•Operations Analysis
•Software Engineering
•Systems Engineering
TRAC-Monterey
USMA
•Systems Engineering
Contractors
•Rolands and Associates, Inc.
•Dynamics Research Corporation
•Tapestry Solutions, Inc.
•NovaLogic Systems
•Wexford Group
Overview
Background
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
•
•
•
•
State of the practice
Data management
Higher resolution model support
Data interchange formats
Overview
Current Data Exchange Methodology
Overview
Data Interchange Formats
Overview
Project Milestones
Consumer Data
Requirements
Analyze
Consumer Data
Requirements
Provider Formats
Data Requirements
Assess Providers
Formats
Producer
Formats
Extend and
Document XML
Data Standard
FY01 Data Standard
Manage Project
FY02 Data Standard
Final Report
Demo
Results
Demonstrate DIF
Use
Sample Producer Data
Sample Consumer Data
Demonstration
Tool
Legend
Develop User
Interface
Control / Constraint
Input
Activity
Output
XML Spy IDE
Mechanism
Overview
FY02 Providers/Consumers
Authoritative Data
Providers
XML Populated DIF
(XPOD)
Consuming
Simulation
Systems
OTB Reader
Files
OTB Import
Routines
AMSAA Export
Routines
DIMSRR SPIRIT
Databases
C&P Data
populated into
XML DIF
Standard
<?xml version=“1.0”?>
<EquipmentCPData>
<tag>data</tag>
</EquipmentCPData>
OOS KA/KE
Repository
OOS Conversion
Routines
NGIC Export
Routines
Combat XXI
Import Routines
Combat XXI
Legacy
Formats
Overview
Scope
The FY01 scope was:
• Ground systems and direct fire
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)
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.
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.
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
<parameter>
<name>weight</name>
<weight>18</weight>
<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
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
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
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
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
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
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
 Provide explicit semantics
• Semantic mapping
responsibility
 Decision: Consumer or
Producer
• Explicit tags vs. Meta-model
approach,
 Compromise; combined data
model
• Standard nomenclature
• Entity type enumerations
 SND and DMSO common
semantics
• Versioning / Traceability
 MIDB before IEEE
• Storage methods
 Provide archive info meta data
• Distribution methods
• Standards development
process
 Limit tag names, spaces and
comments
 Web Services
 SISO
Overview
Questions?
Overview