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models/Application_examples/Agile_Production_Platform_Customer_Interactions.mp
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/* Model of Agile Production Platform with Customer Interactions
Created collaboratively in Teams by Kristin Giammarco,
Michael Collins, and E.Griffor on the 20th of
August, 2020.
Modified collaboratively in Teams by Kristin Giammarco and
Joshua Beaver on the 17th of September, 2020.
Edited by Pamela Dyer in September, 2021.
Purpose: To illustrate a model that facilitates agreement
on a baseline typical use case for a Customer placing an
order using the "Agile Production Platform" (APP). Here
the order is for face shields in response to the COVID-19
pandemic.
Description: This model demonstrates using MP to find both
desired and undesired scenarios of Personal Protective
Equipment (PPE) supply chains within the APP. The APP
represents the linkage platform being designed to enable
agile production / building of alternative supply chains.
Specifically in this model, the focus is on the delivered
quality of 5,000 medical face shields (a Customer placed
the order using the APP). Running this model at Scope 1
generates 4 event traces, described as follows:
Trace 1: The order is delivered to the Customer and
meets their expectations (Favorable Outcome).
Trace 2: The order is delivered to the Customer and
does not meet their expectations (Unfavorable
Outcome, as well as Unexpected Emergent Behavior
that manifested as a counterfeit part which made
its way into the APP supply chain).
Trace 3: The order is not delivered to the Customer
(Unfavorable Outcome, as well as Expected
Emergent Behavior).
Trace 4: The Customer is notified by the APP that the
order cannot be completed at this time because
no alternative supply chain can be found
(Unfavorable Outcome).
References:
Beaver, Joshua. "Analyzing Emergent Behavior of Supply
Chains for Personal Protective Equipment in Response to
COVID-19." NPS Master's Thesis, Monterey, CA:
September 2021.
Search terms: behavior, supply chain; coordination, event;
SAY statement; trace annotation; event sharing; table;
behavior, unexpected; behavior, emergent;
behavior, agile production platform
Instructions: Run for Scope 1.
Scope 1: 4 traces in less than 1 sec.
==========================================================*/
/****I have not edited anything between this next line and the SCHEMA line****/
/***********************************************************************************
Agile_Production_Platform_Customer_Interactions
......@@ -105,7 +162,7 @@ some service provider?
Postcondition: The Customer receives 5,000 face shields (not shown in the model yet).
*************************************************************************************/
/****Up to this line just above should be moved up/edited??****/
SCHEMA Agile_Production_Platform_Customer_Interactions
......
models/Application_examples/Agile_Production_Platform_Supply_Chain_Interaction.mp
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/* Author: Josh Beaver
*/
/* Model of Agile Production Platform with Supply Chain Interaction
Created collaboratively in Teams by Kristin Giammarco,
Michael Collins, and E.Griffor on the 20th of
August, 2020.
Modified collaboratively in Teams by Kristin Giammarco and
Joshua Beaver on the 17th of September, 2020.
Edited by Pamela Dyer in September, 2021.
Purpose: To illustrate a model that focuses on the
interrelations of supply chains with the "Agile Production
Platform" (APP) as the intermediary.
Description: This model demonstrates using MP to find both
desired and undesired scenarios resulting from interactions
among supply chains. Raw material suppliers, component
suppliers, finished product suppliers, the customer, and
the APP are all taken into account. In general, the goal
of the model is to check that each phase of supplier is
able to produce the expected product. Here the product
is a general term. When run at Scope 1, there are 55 traces
generated. An example of Expected Emergent Behavior coming
out of this is: the component supplier was still at maximum
capacity even though the APP pooled more suppliers at all
levels of production, so the order was not fulfilled (an
Unfavorable Outcome). An example of Unexpected Emergent
Behavior coming out of this model is: a nonconforming
material still making its way to the customer, either
through a miscommunication or lack of inspection (also an
Unfavorable Outcome).
References:
Beaver, Joshua. "Analyzing Emergent Behavior of Supply
Chains for Personal Protective Equipment in Response to
COVID-19." NPS Master's Thesis, Monterey, CA:
September 2021.
Search terms: behavior, supply chain; coordination, event;
behavior, unexpected; behavior, emergent;
behavior, agile production platform
Instructions: Run for Scope 1.
Scope 1: 55 traces in less than 1 sec.
==========================================================*/
SCHEMA Agile_Production_Platform_Supply_Chain_Interaction
......
models/Application_examples/Correlation_and_Fusion_Process_Ungoverned.mp
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/* Ungoverned Correlation Model August 2021
created by F Watson 8/20/2021
Purpose:
To model AIS reporting data flows showing existing data path behaviors
Description:
Models the data flow from sensors all the way to the human analyst at a tactical location.
Details
AIS_Sensor: represents an example AIS GPS SENSOR
Aggregation_Service: Represents a local AIS Aggregator like a "Radar" system or local AIS Broadcast
Global_Distribution_Mechanism: Receives data from multiple aggregation services, coalates the data, and rebroadcasts the data globally.
Source A: Represents raw AIS tracks that have not been modified in any way prior to dissemination
Source B: Represents raw AIS tracks grouped into geographic areas for limited broadcast
Source C: Represents Custom filtering and combining of AIS tracks based on unique fields
Correlator functions: Receive_Source_Status
Process_Source_Status
/* Model of Correlation and Fusion Process Ungoverned
Created by Frank Watson on the 20th of August, 2021.
Edited by Pamela Dyer in September, 2021.
Purpose: To model AIS reporting data flows showing existing
data path behaviors.
Description: Models the data flow from sensors all the way
to the human analyst at a tactical location.
Details
AIS_Sensor: represents an example AIS GPS SENSOR
Aggregation_Service: Represents a local AIS Aggregator
like a "Radar" system or local AIS Broadcast
Global_Distribution_Mechanism: Receives data from
multiple aggregation services, coalates the data,
and rebroadcasts the data globally.
Source A: Represents raw AIS tracks that have not been
modified in any way prior to dissemination
Source B: Represents raw AIS tracks grouped into
geographic areas for limited broadcast
Source C: Represents Custom filtering and combining
of AIS tracks based on unique fields
Correlator functions: Receive_Source_Status
Process_Source_Status
Evaluate_Source_Status
Display_Correlated_Tracks
Analyst functions: View_Correlated_Tracks
Analyze_Tracks_Position_Accuracy
Correct_Track_Positions
The events are representative of actual AIS data report flow to operational analysts in the Fleet today.
References:
Example 49 Histogram showing number of traces with probabilities within certain intervals, from Auguston, M. "Monterey Phoenix
System and Software Architecture and Workflow Modeling Language Manual"
(Version 4). 2020. Available online: https://wiki.nps.edu/display/MP/Documentation
Example 40. A global report is assembled from the set of all available event traces, from Auguston, M. "Monterey Phoenix
System and Software Architecture and Workflow Modeling Language Manual"
(Version 4). 2020. Available online: https://wiki.nps.edu/display/MP/Documentation
Search terms: behavior, data correlation and fusion; probability, Type 1; data governance
Instructions:
Run for scope 1: 8 traces in < 1 sec
*/
Analyst functions: View_Correlated_Tracks
Analyze_Tracks_Position_Accuracy
Correct_Track_Positions
The events are representative of actual AIS data report
flow to operational analysts in the Fleet today.
References:
Watson, Frank. "Design Methodologies for 21st Century
Battlefield Object Correlation and Fusion." NPS
Master's Thesis, Monterey, CA: September 2021.
Example 49. Histogram showing number of traces with
probabilities within certain intervals, from Auguston,
M. "Monterey Phoenix System and Software Architecture
and Workflow Modeling Language Manual" (Version 4).
2020. Available online:
https://wiki.nps.edu/display/MP/Documentation
Example 40. A global report is assembled from the set of
all available event traces, from Auguston, M.
"Monterey Phoenix System and Software Architecture
and Workflow Modeling Language Manual" (Version 4).
2020. Available online:
https://wiki.nps.edu/display/MP/Documentation
Search terms: behavior, data correlation and fusion;
probability, Type 1; data governance
Instructions: Run for Scope 1.
Scope 1: 8 traces in less than 1 sec.
==========================================================*/
SCHEMA Correlation_and_Fusion_Process_Ungoverned
......
models/Application_examples/Correlation_and_Fusion_Process_with_Data_Governance.mp
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/* Applied Data Governance Framework Model
created by F Watson 8/20/2021
Purpose:
To model AIS reporting data flows showing updated data path behaviors with the application of a data governance framework
Description:
Models the data flow from sensors all the way to the human analyst at a tactical location incorporating a data governance framework.
Details
AIS_Sensor: represents an example AIS GPS SENSOR
Aggregation_Service: Represents a local AIS Aggregator like a "Radar" system or local AIS Broadcast
Global_Distribution_Mechanism: Receives data from multiple aggregation services, coalates the data, and rebroadcasts the data globally.
Source A: Represents raw AIS tracks that have not been modified in any way prior to dissemination
Source B: Represents raw AIS tracks grouped into geographic areas for limited broadcast
Source C: Represents Custom filtering and combining of AIS tracks based on unique fields
Data Governance Framework applies evaluation rubric to data sources and only allows sources that are uncorrupted to be transmitted to the correlator.
The Data_Governance_Framework receivees sources, evaluates source corruption levels, applies a rubric evaluation, and transmits approved sources to the correlator function
Correlator functions: Receive_Source_Status
Process_Source_Status
/* Model of Correlation and Fusion Process with Data Governance Framework
Created by Frank Watson on the 20th of August, 2021.
Edited by Pamela Dyer in September, 2021.
Purpose: To model AIS reporting data flows showing updated
data path behaviors with the application of a data
governance framework.
Description: Models the data flow from sensors all the way
to the human analyst at a tactical location incorporating
a data governance framework.
Details
AIS_Sensor: represents an example AIS GPS SENSOR
Aggregation_Service: Represents a local AIS Aggregator
like a "Radar" system or local AIS Broadcast
Global_Distribution_Mechanism: Receives data from
multiple aggregation services, coalates the data,
and rebroadcasts the data globally.
Source A: Represents raw AIS tracks that have not been
modified in any way prior to dissemination
Source B: Represents raw AIS tracks grouped into
geographic areas for limited broadcast
Source C: Represents Custom filtering and combining
of AIS tracks based on unique fields
Data Governance Framework applies evaluation rubric
to data sources and only allows sources that are
uncorrupted to be transmitted to the correlator.
The Data_Governance_Framework receives sources,
evaluates source corruption levels, applies a rubric
evaluation, and transmits approved sources to the
correlator function.
Correlator functions: Receive_Source_Status
Process_Source_Status
Evaluate_Source_Status
Display_Correlated_Tracks
Analyst functions: View_Correlated_Tracks
Analyze_Tracks_Position_Accuracy
Correct_Track_Positions
The events are representative of actual AIS data report flow to operational analysts, with the insertion of a data governance framework.
References:
Example 49 Histogram showing number of traces with probabilities within certain intervals, from Auguston, M. "Monterey Phoenix
System and Software Architecture and Workflow Modeling Language Manual"
(Version 4). 2020. Available online: https://wiki.nps.edu/display/MP/Documentation
Example 40. A global report is assembled from the set of all available event traces, from Auguston, M. "Monterey Phoenix
System and Software Architecture and Workflow Modeling Language Manual"
(Version 4). 2020. Available online: https://wiki.nps.edu/display/MP/Documentation
Search terms: behavior, data correlation and fusion; probability, Type 1; data governance
Instructions:
Run for scope 1: 4 traces in < 1 sec
*/
Analyst functions: View_Correlated_Tracks
Analyze_Tracks_Position_Accuracy
Correct_Track_Positions
The events are representative of actual AIS data report
flow to operational analysts, with the insertion of a
data governance framework.
References:
Watson, Frank. "Design Methodologies for 21st Century
Battlefield Object Correlation and Fusion." NPS
Master's Thesis, Monterey, CA: September 2021.
Example 49. Histogram showing number of traces with
probabilities within certain intervals, from Auguston,
M. "Monterey Phoenix System and Software Architecture
and Workflow Modeling Language Manual" (Version 4).
2020. Available online:
https://wiki.nps.edu/display/MP/Documentation
Example 40. A global report is assembled from the set of
all available event traces, from Auguston, M.
"Monterey Phoenix System and Software Architecture
and Workflow Modeling Language Manual" (Version 4).
2020. Available online:
https://wiki.nps.edu/display/MP/Documentation
Search terms: behavior, data correlation and fusion;
probability, Type 1; data governance
Instructions: Run for Scope 1.
Scope 1: 4 traces in less than 1 sec.
==========================================================*/
SCHEMA Correlation_and_Fusion_Process_with_Data_Governance
......
models/Application_examples/Supply_Chain_with_Two_Cyber_Threats.mp
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/*
Two-Cyber-Threats-Model
/* Model of Supply Chain with Two Cyber Threats
Based on the "Baseline Supply Chain" model
created by Nathaniel Alden with help from Rachel Talkington 2020-07-05
updated by Nathaniel Alden and Kristin Giammarco 2020-12-16
modified by Margaret Palmieri, added outcomes
modified by Margaret Palmieri, added risk and probability
modified by Kristin Giammarco 2021-01-14, regrouped attributes and added global report
modified by Mikhail Auguston 2021-01-14, added calculations to global report
modified by Kristin Giammarco 2021-02-25, cleaned up comments
modified by Margaret Palmieri 2021-07-29, added Pipeline use case, updated global report for two scenarios
modified by Kristin Giammarco 2021-08-01, revised global report model for two scenarios
modified by Margaret Palmieri 2021-08-03, updated attributes and model notes
curated for model collection by Kristin Giammarco and Pamela Dyer, 2021-09-20
*/
Created by Nathaniel Alden with help from Rachel Talkington
on the 5th of July, 2020.
Updated by Nathaniel Alden and Kristin Giammarco on the
16th of December, 2020.
Modified by Margaret Palmieri, added outcomes.
Modified by Margaret Palmieri, added risk and probability.
Modified by Kristin Giammarco on the 14th of January, 2021,
regrouped attributes and added global report.
Modified by Mikhail Auguston on the 14th of January, 2021,
added calculations to global report.
Modified by Kristin Giammarco on the 25th of February, 2021,
cleaned up comments.
Modified by Margaret Palmieri on the 29th of July, 2021,
added Pipeline use case, updated global report for
two scenarios.
Modified by Kristin Giammarco on the 1st of August, 2021,
revised global report model for two scenarios.
Modified by Margaret Palmieri on the 3rd of August, 2021,
updated attributes and model notes.
Curated for model collection by Kristin Giammarco and
Pamela Dyer on the 20th of September, 2021.
Purpose: To illustrate the modeling of a supply chain
that is threatened by two separate cyber-attacks, and to
demonstrate calculating individual and total risk scores.
Description: This model demonstrates performing detailed
risk analysis on a supply chain potentially affected by
two cyber threats. This is made possible by combining two
MP models that each contain one threat: a cyber-attack on
the barge, and a cyber-attack on the Colonial Pipeline.
All three of these models can be found in (Palmieri 2021).
This model first takes advantage of separate COORDINATE
statements to calculate and display risk associated with
each use case individually, and then performs calculations
for the total risk to the supply chain from both cyber
threats. Number attributes are used to determine both the
likelihood and impact factors for each case, and then
trace_risk_score combines them to generate the total risk
to the supply chain from both cyber threats. Each trace
that is above a certain threshold value is marked, and a
global risk report is issued. This report contains the
total risk across the total number of traces, highest risk,
average risk, and a direction to sort traces by those
marked first. Decision makers can reference models such as
this when in need of assessing and comparing risk across
multiple scenarios from multiple threats, especially when
it is necessary to best allocate limited resources for
investing in cyber security.
References:
Palmieri, Margaret. "Assessing and Visualizing Risk in
Monterey Phoenix Through a Supply Chain Cyber-Attack Use
Case." NPS Master's Thesis, Monterey, CA: September 2021.
Search terms: behavior, supply chain; coordination, event;
cyber threat; cyber-attack; event attribute, number; table;
trace annotation; risk score; risk analysis; SAY statement;
MARK command; report, global
Instructions: Run for Scope 1.
Scope 1: 12 traces in less than 1 sec.
==========================================================*/
SCHEMA Supply_Chain_with_Two_Cyber_Threats
......
models/Example31_Petri_Net.mp
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/* Example 32. Model of Petri Net
/* Example 31. Model of Petri Net
Purpose: To demonstrate the modeling of a Petri net in MP.
......
models/Example32_ATMWithdrawal_StatechartView.mp
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/* Example 33. Model of ATM Withdrawal with Statechart
/* Example 32. Model of ATM Withdrawal with Statechart
Purpose: To demonstrate how to extract a Statechart view
from an MP model.
......
models/Example33_FiniteStateDiagram_PathAnnotation.mp
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/* Example 34. Model of Finite State Diagram with Path Annotation
/* Example 33. Model of Finite State Diagram with Path Annotation
Purpose: To demonstrate how to 1) model the behavior of a
finite state diagram and 2) generate event traces as
......
models/Example34_FiniteStateDiagram_PathDiagram.mp
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/* Example 35. Model of Finite State Diagram with Path Diagram
/* Example 34. Model of Finite State Diagram with Path Diagram
Purpose: To demonstrate on a model of finite state diagram
behavior how to generate 1) path diagrams on each event trace
......
models/Example35_Authentication_SystemReuse.mp
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/* Example 36. Model of Authentication System
/* Example 35. Model of Authentication System
Purpose: To demonstrate behavior reuse with the MAP
composition operation. Authentication system's behavior
......
models/Example36_Compiler1_ComponentReuse.mp
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/* Example 37. Model of Compiler in Batch Processing Mode
/* Example 36. Model of Compiler in Batch Processing Mode
Purpose: To demonstrate component reuse between models,
emphasizing the advantages of separation between the
specification of component behavior and the specification
of interactions between components.
Description: Examples 37 and 38 are models of a bottom-up
Description: Examples 36 and 37 are models of a bottom-up
parser with lexical analyzer based on regular expression
matching. It models the behavior of Lex/Yacc generated
compiler’s front end. This model represents an architecture
where lexer stores tokens in the intermediary data structure
before parser starts to access it, and Example 38 is a model
before parser starts to access it, and Example 37 is a model
where parser works with the lexer interactively. The Lexer
part models the behavior of a typical Lex machine. The
behavior of Stack is integrated into Parser’s behavior.
......@@ -27,7 +27,7 @@ and the rest of the MP model. Interactions in MP
descriptions (event grammar rules), and such adjustment
can be done in a declarative fashion by coordinating the
reusable MP code and the MP code under development.
Examples 37 and 38 show how it can be done using a model
Examples 36 and 37 show how it can be done using a model
of a compiler.
References:
......
models/Example37_Compiler2_ComponentReuse.mp
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/* Example 38. Model of Compiler in Interactive Mode
/* Example 37. Model of Compiler in Interactive Mode
Purpose: To demonstrate component reuse between models,
emphasizing the advantages of separation between the
specification of component behavior and the specification
of interactions between components.
Description: Examples 37 and 38 are models of a bottom-up
Description: Examples 36 and 37 are models of a bottom-up
parser with lexical analyzer based on regular expression
matching. It models the behavior of Lex/Yacc generated
compiler’s front end. Example 37 represents an
compiler’s front end. Example 36 represents an
architecture where lexer stores tokens in the intermediary
data structure before parser starts to access it, and this
is a model where parser works with the lexer interactively.
......@@ -26,7 +26,7 @@ and the rest of MP model. Interactions in MP (coordination
operations) are separated from the behavior descriptions
(event grammar rules), and such adjustment can be done in
a declarative fashion by coordinating the reusable MP code
and the MP code under development. Examples 37 and 38 show
and the MP code under development. Examples 36 and 37 show
how it can be done using a model of a compiler.
References:
......
models/Example38_Merging_Root_Events_to_Reduce_Run_Time.mp
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/* Example 39. Model of Merging Root Events to Reduce Run Time
/* Example 38. Model of Merging Root Events to Reduce Run Time
Purpose: To demonstrate how to organize a hierarchy of
derivations in order to reduce run time for larger models.
......
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