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?