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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