Model Consistency Checking

Yong Zhao

E-mail yz300uoweduau

Outline

Introduction Consistency constraint Approaches Conclusion amp future work

Abstract

Process portfolio often encodes multiple ways of doing same thing Models may be described at varying levels of detail and varying levels of completeness Thus some models in a process portfolio might be refinements of other models in a process portfolio Some models might describe a fragment of another model These can cause a range of management problems We have defined techniques to determine whether a given set of BPMN models is consistent Then we extend these notions to define a looser but more practical notion of graded model consistency that can involve measuring the degree of similarity between models that violate an absolute test for consistency

Introduction

Why need consistency check

Model play central role in software development

E-Business enterprises collaborate across organizational boundaries

Introduction

Consistency as a basic quality attribute Consistency is crucial A model must be consistency before it is

transformed into other forms Consistency

Between different views Between models at different levels of abstraction

Introduction

Models (descriptions) focus on views corresponding to system parts

class component subsystem 1048708 aspects

data function distribution security 1048708 user views

clerk customer system administrator 1048708 hellip

Introduction

Introduction

Ensuring consistency is very difficult Semantics of the model

Complexity due to multiple views and multiple levels

Introduction

The aim of consistency check is to provide a partial but effective solution to model consistency problem Define a set of constraints that can detect a large

number of common errors in models Design algorithms that can automatically check if

a model satisfies the consistency constraints

Consistency constraints

What are consistent constraints Restriction on the uses of diagrammatic notions

variable and names types and symbols in a modeling language to reduce the possibility of inconsistency

Example The same identifier that occurs at different places

must refer to the same entity An entity should be referred to by the same

identifier if it occurs at different diagrams

Types of Consistency constraints Intra-model consistency within one type of

model Intra-diagram within one diagram Inter-diagram between different diagrams of the

same model Inter-model consistency

Between different types of models hence also different diagrams

Types of Consistency constraints Horizontal consistency

Between modelsdiagrams of the same level of abstraction

Vertical consistency between modelsdiagrams at different levels of abstraction Local between two levels Global with respect to the overall structure

Related work

Marc Ehrig Agnes Koschmider and Andreas Oberweis propose an approach of measuring the similarity between business process models semantically modeled with the Web Ontology Language (OWL)

Yun Lin(2004) examines the conceptual modeling processes by separating concept concerns in problem frame

Marc Ehrig and Agnes Koschmider(2007) measuring the similarity between business process models semantically modeled with the Web Ontology Language

Li Chen provided a method to quantitatively measure the distance and similarity between two process models based on the efforts for model transformation

Approach

Assumptions Name conflict have been solved Abstraction conflict have been solved

Input a pair of process models and process output a similarity measure which is between 0 and 1

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Outline

Introduction Consistency constraint Approaches Conclusion amp future work

Abstract

Process portfolio often encodes multiple ways of doing same thing Models may be described at varying levels of detail and varying levels of completeness Thus some models in a process portfolio might be refinements of other models in a process portfolio Some models might describe a fragment of another model These can cause a range of management problems We have defined techniques to determine whether a given set of BPMN models is consistent Then we extend these notions to define a looser but more practical notion of graded model consistency that can involve measuring the degree of similarity between models that violate an absolute test for consistency

Introduction

Why need consistency check

Model play central role in software development

E-Business enterprises collaborate across organizational boundaries

Introduction

Consistency as a basic quality attribute Consistency is crucial A model must be consistency before it is

transformed into other forms Consistency

Between different views Between models at different levels of abstraction

Introduction

Models (descriptions) focus on views corresponding to system parts

class component subsystem 1048708 aspects

data function distribution security 1048708 user views

clerk customer system administrator 1048708 hellip

Introduction

Introduction

Ensuring consistency is very difficult Semantics of the model

Complexity due to multiple views and multiple levels

Introduction

The aim of consistency check is to provide a partial but effective solution to model consistency problem Define a set of constraints that can detect a large

number of common errors in models Design algorithms that can automatically check if

a model satisfies the consistency constraints

Consistency constraints

What are consistent constraints Restriction on the uses of diagrammatic notions

variable and names types and symbols in a modeling language to reduce the possibility of inconsistency

Example The same identifier that occurs at different places

must refer to the same entity An entity should be referred to by the same

identifier if it occurs at different diagrams

Types of Consistency constraints Intra-model consistency within one type of

model Intra-diagram within one diagram Inter-diagram between different diagrams of the

same model Inter-model consistency

Between different types of models hence also different diagrams

Types of Consistency constraints Horizontal consistency

Between modelsdiagrams of the same level of abstraction

Vertical consistency between modelsdiagrams at different levels of abstraction Local between two levels Global with respect to the overall structure

Related work

Marc Ehrig Agnes Koschmider and Andreas Oberweis propose an approach of measuring the similarity between business process models semantically modeled with the Web Ontology Language (OWL)

Yun Lin(2004) examines the conceptual modeling processes by separating concept concerns in problem frame

Marc Ehrig and Agnes Koschmider(2007) measuring the similarity between business process models semantically modeled with the Web Ontology Language

Li Chen provided a method to quantitatively measure the distance and similarity between two process models based on the efforts for model transformation

Approach

Assumptions Name conflict have been solved Abstraction conflict have been solved

Input a pair of process models and process output a similarity measure which is between 0 and 1

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Abstract

Process portfolio often encodes multiple ways of doing same thing Models may be described at varying levels of detail and varying levels of completeness Thus some models in a process portfolio might be refinements of other models in a process portfolio Some models might describe a fragment of another model These can cause a range of management problems We have defined techniques to determine whether a given set of BPMN models is consistent Then we extend these notions to define a looser but more practical notion of graded model consistency that can involve measuring the degree of similarity between models that violate an absolute test for consistency

Introduction

Why need consistency check

Model play central role in software development

E-Business enterprises collaborate across organizational boundaries

Introduction

Consistency as a basic quality attribute Consistency is crucial A model must be consistency before it is

transformed into other forms Consistency

Between different views Between models at different levels of abstraction

Introduction

Models (descriptions) focus on views corresponding to system parts

class component subsystem 1048708 aspects

data function distribution security 1048708 user views

clerk customer system administrator 1048708 hellip

Introduction

Introduction

Ensuring consistency is very difficult Semantics of the model

Complexity due to multiple views and multiple levels

Introduction

The aim of consistency check is to provide a partial but effective solution to model consistency problem Define a set of constraints that can detect a large

number of common errors in models Design algorithms that can automatically check if

a model satisfies the consistency constraints

Consistency constraints

What are consistent constraints Restriction on the uses of diagrammatic notions

variable and names types and symbols in a modeling language to reduce the possibility of inconsistency

Example The same identifier that occurs at different places

must refer to the same entity An entity should be referred to by the same

identifier if it occurs at different diagrams

Types of Consistency constraints Intra-model consistency within one type of

model Intra-diagram within one diagram Inter-diagram between different diagrams of the

same model Inter-model consistency

Between different types of models hence also different diagrams

Types of Consistency constraints Horizontal consistency

Between modelsdiagrams of the same level of abstraction

Vertical consistency between modelsdiagrams at different levels of abstraction Local between two levels Global with respect to the overall structure

Related work

Marc Ehrig Agnes Koschmider and Andreas Oberweis propose an approach of measuring the similarity between business process models semantically modeled with the Web Ontology Language (OWL)

Yun Lin(2004) examines the conceptual modeling processes by separating concept concerns in problem frame

Marc Ehrig and Agnes Koschmider(2007) measuring the similarity between business process models semantically modeled with the Web Ontology Language

Li Chen provided a method to quantitatively measure the distance and similarity between two process models based on the efforts for model transformation

Approach

Assumptions Name conflict have been solved Abstraction conflict have been solved

Input a pair of process models and process output a similarity measure which is between 0 and 1

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Introduction

Why need consistency check

Model play central role in software development

E-Business enterprises collaborate across organizational boundaries

Introduction

Consistency as a basic quality attribute Consistency is crucial A model must be consistency before it is

transformed into other forms Consistency

Between different views Between models at different levels of abstraction

Introduction

Models (descriptions) focus on views corresponding to system parts

class component subsystem 1048708 aspects

data function distribution security 1048708 user views

clerk customer system administrator 1048708 hellip

Introduction

Introduction

Ensuring consistency is very difficult Semantics of the model

Complexity due to multiple views and multiple levels

Introduction

The aim of consistency check is to provide a partial but effective solution to model consistency problem Define a set of constraints that can detect a large

number of common errors in models Design algorithms that can automatically check if

a model satisfies the consistency constraints

Consistency constraints

What are consistent constraints Restriction on the uses of diagrammatic notions

variable and names types and symbols in a modeling language to reduce the possibility of inconsistency

Example The same identifier that occurs at different places

must refer to the same entity An entity should be referred to by the same

identifier if it occurs at different diagrams

Types of Consistency constraints Intra-model consistency within one type of

model Intra-diagram within one diagram Inter-diagram between different diagrams of the

same model Inter-model consistency

Between different types of models hence also different diagrams

Types of Consistency constraints Horizontal consistency

Between modelsdiagrams of the same level of abstraction

Vertical consistency between modelsdiagrams at different levels of abstraction Local between two levels Global with respect to the overall structure

Related work

Marc Ehrig Agnes Koschmider and Andreas Oberweis propose an approach of measuring the similarity between business process models semantically modeled with the Web Ontology Language (OWL)

Yun Lin(2004) examines the conceptual modeling processes by separating concept concerns in problem frame

Marc Ehrig and Agnes Koschmider(2007) measuring the similarity between business process models semantically modeled with the Web Ontology Language

Li Chen provided a method to quantitatively measure the distance and similarity between two process models based on the efforts for model transformation

Approach

Assumptions Name conflict have been solved Abstraction conflict have been solved

Input a pair of process models and process output a similarity measure which is between 0 and 1

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Introduction

Consistency as a basic quality attribute Consistency is crucial A model must be consistency before it is

transformed into other forms Consistency

Between different views Between models at different levels of abstraction

Introduction

Models (descriptions) focus on views corresponding to system parts

class component subsystem 1048708 aspects

data function distribution security 1048708 user views

clerk customer system administrator 1048708 hellip

Introduction

Introduction

Ensuring consistency is very difficult Semantics of the model

Complexity due to multiple views and multiple levels

Introduction

The aim of consistency check is to provide a partial but effective solution to model consistency problem Define a set of constraints that can detect a large

number of common errors in models Design algorithms that can automatically check if

a model satisfies the consistency constraints

Consistency constraints

What are consistent constraints Restriction on the uses of diagrammatic notions

variable and names types and symbols in a modeling language to reduce the possibility of inconsistency

Example The same identifier that occurs at different places

must refer to the same entity An entity should be referred to by the same

identifier if it occurs at different diagrams

Types of Consistency constraints Intra-model consistency within one type of

model Intra-diagram within one diagram Inter-diagram between different diagrams of the

same model Inter-model consistency

Between different types of models hence also different diagrams

Types of Consistency constraints Horizontal consistency

Between modelsdiagrams of the same level of abstraction

Vertical consistency between modelsdiagrams at different levels of abstraction Local between two levels Global with respect to the overall structure

Related work

Marc Ehrig Agnes Koschmider and Andreas Oberweis propose an approach of measuring the similarity between business process models semantically modeled with the Web Ontology Language (OWL)

Yun Lin(2004) examines the conceptual modeling processes by separating concept concerns in problem frame

Marc Ehrig and Agnes Koschmider(2007) measuring the similarity between business process models semantically modeled with the Web Ontology Language

Li Chen provided a method to quantitatively measure the distance and similarity between two process models based on the efforts for model transformation

Approach

Assumptions Name conflict have been solved Abstraction conflict have been solved

Input a pair of process models and process output a similarity measure which is between 0 and 1

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Introduction

Models (descriptions) focus on views corresponding to system parts

class component subsystem 1048708 aspects

data function distribution security 1048708 user views

clerk customer system administrator 1048708 hellip

Introduction

Introduction

Ensuring consistency is very difficult Semantics of the model

Complexity due to multiple views and multiple levels

Introduction

The aim of consistency check is to provide a partial but effective solution to model consistency problem Define a set of constraints that can detect a large

number of common errors in models Design algorithms that can automatically check if

a model satisfies the consistency constraints

Consistency constraints

What are consistent constraints Restriction on the uses of diagrammatic notions

variable and names types and symbols in a modeling language to reduce the possibility of inconsistency

Example The same identifier that occurs at different places

must refer to the same entity An entity should be referred to by the same

identifier if it occurs at different diagrams

Types of Consistency constraints Intra-model consistency within one type of

model Intra-diagram within one diagram Inter-diagram between different diagrams of the

same model Inter-model consistency

Between different types of models hence also different diagrams

Types of Consistency constraints Horizontal consistency

Between modelsdiagrams of the same level of abstraction

Vertical consistency between modelsdiagrams at different levels of abstraction Local between two levels Global with respect to the overall structure

Related work

Marc Ehrig Agnes Koschmider and Andreas Oberweis propose an approach of measuring the similarity between business process models semantically modeled with the Web Ontology Language (OWL)

Yun Lin(2004) examines the conceptual modeling processes by separating concept concerns in problem frame

Marc Ehrig and Agnes Koschmider(2007) measuring the similarity between business process models semantically modeled with the Web Ontology Language

Li Chen provided a method to quantitatively measure the distance and similarity between two process models based on the efforts for model transformation

Approach

Assumptions Name conflict have been solved Abstraction conflict have been solved

Input a pair of process models and process output a similarity measure which is between 0 and 1

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Introduction

Introduction

Ensuring consistency is very difficult Semantics of the model

Complexity due to multiple views and multiple levels

Introduction

The aim of consistency check is to provide a partial but effective solution to model consistency problem Define a set of constraints that can detect a large

number of common errors in models Design algorithms that can automatically check if

a model satisfies the consistency constraints

Consistency constraints

What are consistent constraints Restriction on the uses of diagrammatic notions

variable and names types and symbols in a modeling language to reduce the possibility of inconsistency

Example The same identifier that occurs at different places

must refer to the same entity An entity should be referred to by the same

identifier if it occurs at different diagrams

Types of Consistency constraints Intra-model consistency within one type of

model Intra-diagram within one diagram Inter-diagram between different diagrams of the

same model Inter-model consistency

Between different types of models hence also different diagrams

Types of Consistency constraints Horizontal consistency

Between modelsdiagrams of the same level of abstraction

Vertical consistency between modelsdiagrams at different levels of abstraction Local between two levels Global with respect to the overall structure

Related work

Marc Ehrig Agnes Koschmider and Andreas Oberweis propose an approach of measuring the similarity between business process models semantically modeled with the Web Ontology Language (OWL)

Yun Lin(2004) examines the conceptual modeling processes by separating concept concerns in problem frame

Marc Ehrig and Agnes Koschmider(2007) measuring the similarity between business process models semantically modeled with the Web Ontology Language

Li Chen provided a method to quantitatively measure the distance and similarity between two process models based on the efforts for model transformation

Approach

Assumptions Name conflict have been solved Abstraction conflict have been solved

Input a pair of process models and process output a similarity measure which is between 0 and 1

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Introduction

Ensuring consistency is very difficult Semantics of the model

Complexity due to multiple views and multiple levels

Introduction

The aim of consistency check is to provide a partial but effective solution to model consistency problem Define a set of constraints that can detect a large

number of common errors in models Design algorithms that can automatically check if

a model satisfies the consistency constraints

Consistency constraints

What are consistent constraints Restriction on the uses of diagrammatic notions

variable and names types and symbols in a modeling language to reduce the possibility of inconsistency

Example The same identifier that occurs at different places

must refer to the same entity An entity should be referred to by the same

identifier if it occurs at different diagrams

Types of Consistency constraints Intra-model consistency within one type of

model Intra-diagram within one diagram Inter-diagram between different diagrams of the

same model Inter-model consistency

Between different types of models hence also different diagrams

Types of Consistency constraints Horizontal consistency

Between modelsdiagrams of the same level of abstraction

Vertical consistency between modelsdiagrams at different levels of abstraction Local between two levels Global with respect to the overall structure

Related work

Marc Ehrig Agnes Koschmider and Andreas Oberweis propose an approach of measuring the similarity between business process models semantically modeled with the Web Ontology Language (OWL)

Yun Lin(2004) examines the conceptual modeling processes by separating concept concerns in problem frame

Marc Ehrig and Agnes Koschmider(2007) measuring the similarity between business process models semantically modeled with the Web Ontology Language

Li Chen provided a method to quantitatively measure the distance and similarity between two process models based on the efforts for model transformation

Approach

Assumptions Name conflict have been solved Abstraction conflict have been solved

Input a pair of process models and process output a similarity measure which is between 0 and 1

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Introduction

The aim of consistency check is to provide a partial but effective solution to model consistency problem Define a set of constraints that can detect a large

number of common errors in models Design algorithms that can automatically check if

a model satisfies the consistency constraints

Consistency constraints

What are consistent constraints Restriction on the uses of diagrammatic notions

variable and names types and symbols in a modeling language to reduce the possibility of inconsistency

Example The same identifier that occurs at different places

must refer to the same entity An entity should be referred to by the same

identifier if it occurs at different diagrams

Types of Consistency constraints Intra-model consistency within one type of

model Intra-diagram within one diagram Inter-diagram between different diagrams of the

same model Inter-model consistency

Between different types of models hence also different diagrams

Types of Consistency constraints Horizontal consistency

Between modelsdiagrams of the same level of abstraction

Vertical consistency between modelsdiagrams at different levels of abstraction Local between two levels Global with respect to the overall structure

Related work

Marc Ehrig Agnes Koschmider and Andreas Oberweis propose an approach of measuring the similarity between business process models semantically modeled with the Web Ontology Language (OWL)

Yun Lin(2004) examines the conceptual modeling processes by separating concept concerns in problem frame

Marc Ehrig and Agnes Koschmider(2007) measuring the similarity between business process models semantically modeled with the Web Ontology Language

Li Chen provided a method to quantitatively measure the distance and similarity between two process models based on the efforts for model transformation

Approach

Assumptions Name conflict have been solved Abstraction conflict have been solved

Input a pair of process models and process output a similarity measure which is between 0 and 1

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Consistency constraints

What are consistent constraints Restriction on the uses of diagrammatic notions

variable and names types and symbols in a modeling language to reduce the possibility of inconsistency

Example The same identifier that occurs at different places

must refer to the same entity An entity should be referred to by the same

identifier if it occurs at different diagrams

Types of Consistency constraints Intra-model consistency within one type of

model Intra-diagram within one diagram Inter-diagram between different diagrams of the

same model Inter-model consistency

Between different types of models hence also different diagrams

Types of Consistency constraints Horizontal consistency

Between modelsdiagrams of the same level of abstraction

Vertical consistency between modelsdiagrams at different levels of abstraction Local between two levels Global with respect to the overall structure

Related work

Marc Ehrig Agnes Koschmider and Andreas Oberweis propose an approach of measuring the similarity between business process models semantically modeled with the Web Ontology Language (OWL)

Yun Lin(2004) examines the conceptual modeling processes by separating concept concerns in problem frame

Marc Ehrig and Agnes Koschmider(2007) measuring the similarity between business process models semantically modeled with the Web Ontology Language

Li Chen provided a method to quantitatively measure the distance and similarity between two process models based on the efforts for model transformation

Approach

Assumptions Name conflict have been solved Abstraction conflict have been solved

Input a pair of process models and process output a similarity measure which is between 0 and 1

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Types of Consistency constraints Intra-model consistency within one type of

model Intra-diagram within one diagram Inter-diagram between different diagrams of the

same model Inter-model consistency

Between different types of models hence also different diagrams

Types of Consistency constraints Horizontal consistency

Between modelsdiagrams of the same level of abstraction

Vertical consistency between modelsdiagrams at different levels of abstraction Local between two levels Global with respect to the overall structure

Related work

Marc Ehrig Agnes Koschmider and Andreas Oberweis propose an approach of measuring the similarity between business process models semantically modeled with the Web Ontology Language (OWL)

Yun Lin(2004) examines the conceptual modeling processes by separating concept concerns in problem frame

Marc Ehrig and Agnes Koschmider(2007) measuring the similarity between business process models semantically modeled with the Web Ontology Language

Li Chen provided a method to quantitatively measure the distance and similarity between two process models based on the efforts for model transformation

Approach

Assumptions Name conflict have been solved Abstraction conflict have been solved

Input a pair of process models and process output a similarity measure which is between 0 and 1

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Types of Consistency constraints Horizontal consistency

Between modelsdiagrams of the same level of abstraction

Vertical consistency between modelsdiagrams at different levels of abstraction Local between two levels Global with respect to the overall structure

Related work

Marc Ehrig Agnes Koschmider and Andreas Oberweis propose an approach of measuring the similarity between business process models semantically modeled with the Web Ontology Language (OWL)

Yun Lin(2004) examines the conceptual modeling processes by separating concept concerns in problem frame

Marc Ehrig and Agnes Koschmider(2007) measuring the similarity between business process models semantically modeled with the Web Ontology Language

Li Chen provided a method to quantitatively measure the distance and similarity between two process models based on the efforts for model transformation

Approach

Assumptions Name conflict have been solved Abstraction conflict have been solved

Input a pair of process models and process output a similarity measure which is between 0 and 1

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Related work

Marc Ehrig Agnes Koschmider and Andreas Oberweis propose an approach of measuring the similarity between business process models semantically modeled with the Web Ontology Language (OWL)

Yun Lin(2004) examines the conceptual modeling processes by separating concept concerns in problem frame

Marc Ehrig and Agnes Koschmider(2007) measuring the similarity between business process models semantically modeled with the Web Ontology Language

Li Chen provided a method to quantitatively measure the distance and similarity between two process models based on the efforts for model transformation

Approach

Assumptions Name conflict have been solved Abstraction conflict have been solved

Input a pair of process models and process output a similarity measure which is between 0 and 1

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Approach

Assumptions Name conflict have been solved Abstraction conflict have been solved

Input a pair of process models and process output a similarity measure which is between 0 and 1

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Approach 1

Node lt ID nodetype owner gt

Edge ltltu vgt edgetype gt

d Stand for diagraphs | d | total number nodes and edges in d

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Approach 1

Parse models from XML Encode the process models into diagraphs di

and dj

Computer total number of nodes plus edge on which two diagraphs thus obtained agree denoted by | intersect (di dj) |

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Approach 1

Similarity measure min(| intersect (di dj) || di | | intersect (di dj) || dj

|)

Threshold Tunable parameter

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Approach 1

Model i and model j is consistent iff The sub-graphs within dI and dj defined by the nod

es common to dI and dj are isomorphic For each incoming edge connecting a common no

de to a node that does not belong to the intersection in one diagraph there does not exist a corresponding incoming edge connecting the same common node in the other

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Approach 2

Combination measurement Syntactic Similarity Measure

ed edit distance |c| length of c

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Approach 2

Linguistic Similarity Measure ƞ(c) phrasing of the given ontological concept inst

ance c Let S = ƞ(c1) cap ƞ(c2) Let max(|ƞ (c1)| |ƞ (c2)|) be the maximum of the c

ardinalities of the two sets ƞ(c1) and ƞ(c2)

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Approach 2

Structural Similarity Measure simki (c1ic2j)denotes the specific similarity measure used for

the context elements of c1 and c2 which we multiply with individual weights

This measure returns a similarity degree of 10 if the syntactical andor linguistic similarity for the context elements equals 10

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Approach 2

Combined Similarity Measure

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Example

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Example

For approach 1 Min(721717)asymp033

For approach 2 simSPBM asymp032

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Example

Approach 1 only structural measure no semantic measure

While approach 2 only nodes

Could not say which is more better

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Conclusion

My work

Implementation of algorithm 1 in Eclipse

Detail evaluation of the tool using industry-scale cases

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Future work

Improve the algorithm

Re-design and modification of the toolkit based on evaluating results

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

Reference

Lin Y 2004 Applying Problem Frames to Modeling lsquoAbstractionrsquo Concepts In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004) Edinburgh Scotland May 2004

Krogstie J Veres C amp Sindre G 2005 ldquoInteroperability Through Integrating Semantic Web Technology Web Services and Workflow Modelingrdquo In Proc 1st International Conf on Interoperability of Enterprise Software and Applications Feb2005

Lin Y amp Strasunskas D 2005 ldquoOntology-based Semantic Annotation of Process Templates for Reuserdquo In Proc 10th Intl Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSADrsquo05) Porto Portugal 2005

MarcE AgnesK amp AndreasO 2007 Measuring similarity between semantic business process models Proceedings of the fourth Asia-Pacific conference on Comceptual modelling p71-80 January 30-February 02 2007 Ballarat Australia

Li C Reichert M amp Wombacher A 2008 On Measuring Process Model Similarity based on High-level Change Operations In 27th International Conference on Conceptual Modeling (ER08) October 2008 Barcelona Spain

helliphellip

End

Thank you

End

Thank you