Merge branch 'Pamela_Branch_2' into 'Pamela_Branch_3'

2 into 3

See merge request !39

models/Application_examples/Agile_Production_Platform_Customer_Interactions.mp

+/* 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

-/* 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

-/* 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

-/* 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

-/*
-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

-/* 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

-/* 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

-/*  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

-/* 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

-/* 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

-/* 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

-/* 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

-/* 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.