SlideShare a Scribd company logo
1 of 23
Diagnosing and RepairingData Anomalies in Process ModelsAhmed AwadHassoPlattner Institute, Potsdam, GermanyGero Decker		HassoPlattner Institute, Potsdam, GermanyNielsLohmann		University of Rostock, Germany
Correctness of Process Models widely accepted: soundness no deadlocks no livelocks proper termination no dead activities These are control flow aspects! Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Example Process: Insurance Claim Handling sound: every claim will eventually be closed Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Data in BPMN Data objects Data states (no explosion) Object life cycles /control flow refinement Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Process Model with Data This model contains five deadlocks! Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Classes of Errors too restrictive preconditions (close and fraudulent claims) implicit routing (XOR vs. fraud evaluation) implicit execution order (pay vs. file) Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Outline control flow + data flow = interesting problems ✔ formalization BPMN’s data aspects detection, diagnosing, and repairing of data anomalies Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
2 FormalizationBPMN’s dataaspects
BPMN and Petri nets BPMN: a graphical notion support of concurrency Petri nets: a graphical notion support of concurrency formal foundation broad tool support Diagnosing and Repairing Data Anomalies in Process Models 07.09.09 Dijkman et al. definePetri net semanticsfor BPMN’s control flow
Petri net formalization (control flow) pattern-based translation complete example (control flow): analysis tools can check soundness Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
BPMN and Petri nets (2) BPMN: a graphical notion support of concurrency several aspects in one model Petri nets: a graphical notion support of concurrency formal foundation broad tool support simple composition notions Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Formalization of BPMN’s data objects changing a state reading a state changing to several possible states Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Data flow models data flow models for settlement and claim data object control flow model Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Bringing it all together synchronization of data flow and control flow by transition fusion Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
3 Detection,diagnosing,and repair ofdata anomalies
Detection of data anomalies standard soundness checker (Woflan, LoLA) will find deadlocks provides counterexample (= trace) does not differentiate data flow and control flow gives no diagnosis/repair information Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Diagnosis data anomalies exploit information on model: control flow is sound place models either control flow or data flow each data object can only be in one state Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Diagnosing and fixing too restrictive preconditions Problem: if data is set to [a], activity B is disabled Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Diagnosing and fixing too restrictive preconditions control flow is sound deadlock in composite model: missing data tokens for each deadlock: determine missing data tokens change model data tokens are present, or drop data dependency Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Diagnosing and fixing implicit routing decision [b] vs. [c] has to be synchronized with XOR-split Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Diagnosing and fixing implicit routing partition state space with respect to data states if a decision inside a partition leads to a deadlock, this decision is “unsynchronized” synchronize decisions according to data Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Diagnosing and fixing implicit execution order transitions A and B are in concurrent the control flow model,but share data place Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
Take home points data objects can introduce errors to a model Petri nets allow for compositional models of data and control flow data anomalies can be detected, diagnosed,and (sometimes) automatically fixed Future work: automated mapping back to the BPMN model Diagnosing and Repairing Data Anomalies in Process Models 07.09.09

More Related Content

Viewers also liked (6)

succession18.1_18.5.ppt
succession18.1_18.5.pptsuccession18.1_18.5.ppt
succession18.1_18.5.ppt
 
QUIZ BY - PRAGYAN YADAV
QUIZ BY - PRAGYAN YADAVQUIZ BY - PRAGYAN YADAV
QUIZ BY - PRAGYAN YADAV
 
Laporan tugas ketiga
Laporan tugas ketigaLaporan tugas ketiga
Laporan tugas ketiga
 
QUIZ BY - PRAGYAN YADAV
QUIZ BY - PRAGYAN YADAVQUIZ BY - PRAGYAN YADAV
QUIZ BY - PRAGYAN YADAV
 
Folder A4 Cachaça Sapucaia
Folder A4 Cachaça SapucaiaFolder A4 Cachaça Sapucaia
Folder A4 Cachaça Sapucaia
 
Basic marketingresearch
Basic marketingresearchBasic marketingresearch
Basic marketingresearch
 

Similar to Diagnosing and Repairing Data Anomalies in Process Models

Anomaly Detection using multidimensional reduction Principal Component Analysis
Anomaly Detection using multidimensional reduction Principal Component AnalysisAnomaly Detection using multidimensional reduction Principal Component Analysis
Anomaly Detection using multidimensional reduction Principal Component Analysis
IOSR Journals
 
ISEN 613_Team3_Final Project Report
ISEN 613_Team3_Final Project ReportISEN 613_Team3_Final Project Report
ISEN 613_Team3_Final Project Report
Naman Kapoor
 
ISEN 613_Team3_Final Project Report
ISEN 613_Team3_Final Project ReportISEN 613_Team3_Final Project Report
ISEN 613_Team3_Final Project Report
Rahul Garg, CSSGB
 
COSMOS-ASME-IDETC-2014
COSMOS-ASME-IDETC-2014COSMOS-ASME-IDETC-2014
COSMOS-ASME-IDETC-2014
OptiModel
 
Detecting Discontinuties in Large Scale Systems
Detecting  Discontinuties in Large Scale SystemsDetecting  Discontinuties in Large Scale Systems
Detecting Discontinuties in Large Scale Systems
haroonmalik786
 

Similar to Diagnosing and Repairing Data Anomalies in Process Models (20)

Anomaly Detection using multidimensional reduction Principal Component Analysis
Anomaly Detection using multidimensional reduction Principal Component AnalysisAnomaly Detection using multidimensional reduction Principal Component Analysis
Anomaly Detection using multidimensional reduction Principal Component Analysis
 
Anomaly detection via online over sampling principal component analysis
Anomaly detection via online over sampling principal component analysisAnomaly detection via online over sampling principal component analysis
Anomaly detection via online over sampling principal component analysis
 
Image processing-ieee-2014-projects
Image processing-ieee-2014-projectsImage processing-ieee-2014-projects
Image processing-ieee-2014-projects
 
Image Processing IEEE 2014 Projects
Image Processing IEEE 2014 ProjectsImage Processing IEEE 2014 Projects
Image Processing IEEE 2014 Projects
 
IEEE 2014 Matlab Projects
IEEE 2014 Matlab ProjectsIEEE 2014 Matlab Projects
IEEE 2014 Matlab Projects
 
IEEE 2014 Matlab Projects
IEEE 2014 Matlab ProjectsIEEE 2014 Matlab Projects
IEEE 2014 Matlab Projects
 
Traffic Simulator
Traffic SimulatorTraffic Simulator
Traffic Simulator
 
ISEN 613_Team3_Final Project Report
ISEN 613_Team3_Final Project ReportISEN 613_Team3_Final Project Report
ISEN 613_Team3_Final Project Report
 
ISEN 613_Team3_Final Project Report
ISEN 613_Team3_Final Project ReportISEN 613_Team3_Final Project Report
ISEN 613_Team3_Final Project Report
 
On Error Injection for NoC Platforms: A UVM-based Practical Case Study
On Error Injection for NoC Platforms: A UVM-based Practical Case StudyOn Error Injection for NoC Platforms: A UVM-based Practical Case Study
On Error Injection for NoC Platforms: A UVM-based Practical Case Study
 
The unknown spatial quality of dense point clouds derived from stereo images
The unknown spatial quality of dense point clouds derived from stereo imagesThe unknown spatial quality of dense point clouds derived from stereo images
The unknown spatial quality of dense point clouds derived from stereo images
 
Network predictive analysis
Network predictive analysisNetwork predictive analysis
Network predictive analysis
 
A Hierarchical Feature Set optimization for effective code change based Defec...
A Hierarchical Feature Set optimization for effective code change based Defec...A Hierarchical Feature Set optimization for effective code change based Defec...
A Hierarchical Feature Set optimization for effective code change based Defec...
 
IRJET- Front View Identification of Vehicles by using Machine Learning Te...
IRJET-  	  Front View Identification of Vehicles by using Machine Learning Te...IRJET-  	  Front View Identification of Vehicles by using Machine Learning Te...
IRJET- Front View Identification of Vehicles by using Machine Learning Te...
 
COSMOS-ASME-IDETC-2014
COSMOS-ASME-IDETC-2014COSMOS-ASME-IDETC-2014
COSMOS-ASME-IDETC-2014
 
Programming with Relaxed Synchronization
Programming with Relaxed SynchronizationProgramming with Relaxed Synchronization
Programming with Relaxed Synchronization
 
Detecting Discontinuties in Large Scale Systems
Detecting  Discontinuties in Large Scale SystemsDetecting  Discontinuties in Large Scale Systems
Detecting Discontinuties in Large Scale Systems
 
Parallel machines flinkforward2017
Parallel machines flinkforward2017Parallel machines flinkforward2017
Parallel machines flinkforward2017
 
DDI 3D Medical Prosthetics Presentation to AAA Conference, April 2007
DDI 3D Medical Prosthetics Presentation to AAA Conference, April 2007DDI 3D Medical Prosthetics Presentation to AAA Conference, April 2007
DDI 3D Medical Prosthetics Presentation to AAA Conference, April 2007
 
Cost Estimation Predictive Modeling: Regression versus Neural Network
Cost Estimation Predictive Modeling: Regression versus Neural NetworkCost Estimation Predictive Modeling: Regression versus Neural Network
Cost Estimation Predictive Modeling: Regression versus Neural Network
 

More from Universität Rostock

Verification with LoLA: 7 Implementation
Verification with LoLA: 7 ImplementationVerification with LoLA: 7 Implementation
Verification with LoLA: 7 Implementation
Universität Rostock
 
Verification with LoLA: 6 Integrating LoLA
Verification with LoLA: 6 Integrating LoLAVerification with LoLA: 6 Integrating LoLA
Verification with LoLA: 6 Integrating LoLA
Universität Rostock
 
Verification with LoLA: 5 Case Studies
Verification with LoLA: 5 Case StudiesVerification with LoLA: 5 Case Studies
Verification with LoLA: 5 Case Studies
Universität Rostock
 
Verification with LoLA: 4 Using LoLA
Verification with LoLA: 4 Using LoLAVerification with LoLA: 4 Using LoLA
Verification with LoLA: 4 Using LoLA
Universität Rostock
 
Verification with LoLA: 3 State Space Reduction
Verification with LoLA: 3 State Space ReductionVerification with LoLA: 3 State Space Reduction
Verification with LoLA: 3 State Space Reduction
Universität Rostock
 
Verification with LoLA: 2 The LoLA Input Language
Verification with LoLA: 2 The LoLA Input LanguageVerification with LoLA: 2 The LoLA Input Language
Verification with LoLA: 2 The LoLA Input Language
Universität Rostock
 
Karsten Wolf @ Carl Adam Petri Memorial Symposium
Karsten Wolf @ Carl Adam Petri Memorial SymposiumKarsten Wolf @ Carl Adam Petri Memorial Symposium
Karsten Wolf @ Carl Adam Petri Memorial Symposium
Universität Rostock
 

More from Universität Rostock (20)

Pragmatic model checking: from theory to implementations
Pragmatic model checking: from theory to implementationsPragmatic model checking: from theory to implementations
Pragmatic model checking: from theory to implementations
 
Where did I go wrong? Explaining errors in process models
Where did I go wrong? Explaining errors in process modelsWhere did I go wrong? Explaining errors in process models
Where did I go wrong? Explaining errors in process models
 
Decidability Results for Choreography Realization
Decidability Results for Choreography RealizationDecidability Results for Choreography Realization
Decidability Results for Choreography Realization
 
Artifact-centric modeling using BPMN
Artifact-centric modeling using BPMNArtifact-centric modeling using BPMN
Artifact-centric modeling using BPMN
 
Compliance by Design for Artifact-Centric Business Processes
Compliance by Design for Artifact-Centric Business ProcessesCompliance by Design for Artifact-Centric Business Processes
Compliance by Design for Artifact-Centric Business Processes
 
Verification with LoLA
Verification with LoLAVerification with LoLA
Verification with LoLA
 
Verification with LoLA: 7 Implementation
Verification with LoLA: 7 ImplementationVerification with LoLA: 7 Implementation
Verification with LoLA: 7 Implementation
 
Verification with LoLA: 6 Integrating LoLA
Verification with LoLA: 6 Integrating LoLAVerification with LoLA: 6 Integrating LoLA
Verification with LoLA: 6 Integrating LoLA
 
Verification with LoLA: 5 Case Studies
Verification with LoLA: 5 Case StudiesVerification with LoLA: 5 Case Studies
Verification with LoLA: 5 Case Studies
 
Verification with LoLA: 4 Using LoLA
Verification with LoLA: 4 Using LoLAVerification with LoLA: 4 Using LoLA
Verification with LoLA: 4 Using LoLA
 
Verification with LoLA: 3 State Space Reduction
Verification with LoLA: 3 State Space ReductionVerification with LoLA: 3 State Space Reduction
Verification with LoLA: 3 State Space Reduction
 
Verification with LoLA: 1 Basics
Verification with LoLA: 1 BasicsVerification with LoLA: 1 Basics
Verification with LoLA: 1 Basics
 
Verification with LoLA: 2 The LoLA Input Language
Verification with LoLA: 2 The LoLA Input LanguageVerification with LoLA: 2 The LoLA Input Language
Verification with LoLA: 2 The LoLA Input Language
 
Saarbruecken
SaarbrueckenSaarbruecken
Saarbruecken
 
Ws4 dsec talk @ Kickoff RS3
Ws4 dsec talk @ Kickoff RS3Ws4 dsec talk @ Kickoff RS3
Ws4 dsec talk @ Kickoff RS3
 
Internal Behavior Reduction for Services
Internal Behavior Reduction for ServicesInternal Behavior Reduction for Services
Internal Behavior Reduction for Services
 
Karsten Wolf @ Carl Adam Petri Memorial Symposium
Karsten Wolf @ Carl Adam Petri Memorial SymposiumKarsten Wolf @ Carl Adam Petri Memorial Symposium
Karsten Wolf @ Carl Adam Petri Memorial Symposium
 
Implementation of an Interleaving Semantics for TLDA
Implementation of an Interleaving Semantics for TLDAImplementation of an Interleaving Semantics for TLDA
Implementation of an Interleaving Semantics for TLDA
 
Formale Fundierung und effizientere Implementierung der schrittbasierten TLDA...
Formale Fundierung und effizientere Implementierung der schrittbasierten TLDA...Formale Fundierung und effizientere Implementierung der schrittbasierten TLDA...
Formale Fundierung und effizientere Implementierung der schrittbasierten TLDA...
 
Demonstration of BPEL2oWFN and Fiona
Demonstration of BPEL2oWFN and FionaDemonstration of BPEL2oWFN and Fiona
Demonstration of BPEL2oWFN and Fiona
 

Recently uploaded

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Joaquim Jorge
 

Recently uploaded (20)

Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
 
Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 

Diagnosing and Repairing Data Anomalies in Process Models

  • 1. Diagnosing and RepairingData Anomalies in Process ModelsAhmed AwadHassoPlattner Institute, Potsdam, GermanyGero Decker HassoPlattner Institute, Potsdam, GermanyNielsLohmann University of Rostock, Germany
  • 2. Correctness of Process Models widely accepted: soundness no deadlocks no livelocks proper termination no dead activities These are control flow aspects! Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 3. Example Process: Insurance Claim Handling sound: every claim will eventually be closed Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 4. Data in BPMN Data objects Data states (no explosion) Object life cycles /control flow refinement Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 5. Process Model with Data This model contains five deadlocks! Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 6. Classes of Errors too restrictive preconditions (close and fraudulent claims) implicit routing (XOR vs. fraud evaluation) implicit execution order (pay vs. file) Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 7. Outline control flow + data flow = interesting problems ✔ formalization BPMN’s data aspects detection, diagnosing, and repairing of data anomalies Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 9. BPMN and Petri nets BPMN: a graphical notion support of concurrency Petri nets: a graphical notion support of concurrency formal foundation broad tool support Diagnosing and Repairing Data Anomalies in Process Models 07.09.09 Dijkman et al. definePetri net semanticsfor BPMN’s control flow
  • 10. Petri net formalization (control flow) pattern-based translation complete example (control flow): analysis tools can check soundness Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 11. BPMN and Petri nets (2) BPMN: a graphical notion support of concurrency several aspects in one model Petri nets: a graphical notion support of concurrency formal foundation broad tool support simple composition notions Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 12. Formalization of BPMN’s data objects changing a state reading a state changing to several possible states Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 13. Data flow models data flow models for settlement and claim data object control flow model Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 14. Bringing it all together synchronization of data flow and control flow by transition fusion Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 16. Detection of data anomalies standard soundness checker (Woflan, LoLA) will find deadlocks provides counterexample (= trace) does not differentiate data flow and control flow gives no diagnosis/repair information Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 17. Diagnosis data anomalies exploit information on model: control flow is sound place models either control flow or data flow each data object can only be in one state Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 18. Diagnosing and fixing too restrictive preconditions Problem: if data is set to [a], activity B is disabled Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 19. Diagnosing and fixing too restrictive preconditions control flow is sound deadlock in composite model: missing data tokens for each deadlock: determine missing data tokens change model data tokens are present, or drop data dependency Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 20. Diagnosing and fixing implicit routing decision [b] vs. [c] has to be synchronized with XOR-split Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 21. Diagnosing and fixing implicit routing partition state space with respect to data states if a decision inside a partition leads to a deadlock, this decision is “unsynchronized” synchronize decisions according to data Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 22. Diagnosing and fixing implicit execution order transitions A and B are in concurrent the control flow model,but share data place Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 23. Take home points data objects can introduce errors to a model Petri nets allow for compositional models of data and control flow data anomalies can be detected, diagnosed,and (sometimes) automatically fixed Future work: automated mapping back to the BPMN model Diagnosing and Repairing Data Anomalies in Process Models 07.09.09
  • 24. Final slide Thank you for your attention! Your todos: discuss with me talk to Ahmed and Gero attend the soundness talk(Thursday, after the keynote) get the slides athttp://slideshare.net/correctsystems Diagnosing and Repairing Data Anomalies in Process Models 07.09.09