Download Presentation

Loading in 3 Seconds

This presentation is the property of its rightful owner.

X

Sponsored Links

- 81 Views
- Uploaded on
- Presentation posted in: General

Behavioral Comparison of Process Models Based on Canonically Reduced Event Structures

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Behavioral Comparison of Process Models Based on Canonically Reduced Event Structures

Paolo Baldan

Marlon Dumas

Luciano García

Abel Armas

- Explain the differences between a pair of process models using simple and intuitive statements
- Abstract representations based on binary behavioral relations
- Event structures, e.g., PES and AES

- More expressive formalisms can give smaller representations
- AES can provide smaller representations than PES

- Folding technique does not ensure canonicity
- Canonical graph labeling technique

- Process models can represent infinite behavior. I.e., cyclic behavior.
- Unfolding technique for computing a finite representation

- Provide understandable feedback about behavioral discrepancies
- Error tolerant graph matching techniques
- Categorization of discrepancies

Silent transition

Transition

- Markings: {{p0}, {p1}, {p2}, …}
- Firing sequence: {{a,b, …}, …}
- Executions: {{a,b,c,d}, …}

Place

Configurations: {{a},{a,b},{a,c},{a,b,c}, …}

- AES is a more expressive formalism than PES
- Same configurations as PES, but fewer events
- Reduction technique (folding)
- hp-bisimilarity

- Non-canonicity

- Canonical graph labeling techniques (McKay‘s algorithm)
- Associates a graph with a canonical label
- Largest lexicographical exemplar of the (string linear representation) adjacency matrix

- Associates a graph with a canonical label
- Keep the order given to the vertices in the largest exemplar
- Compute the canonical graph labeling for PES
- Weight of the events

- Folding of events
- Lexicographic order on the event’s label
- Largest set of events
- Largest weights w.r.t. the canonical graph labeling

- Infinite number of events in branching process
- Infinite number of events in PES
- Finite complete prefix unfoldings

- McMillan and Esparza
- Truncating techniques based on markings

- Does not reflect all the possible causal predecessors for any event

- Khomenko et al. proposes a framework to define a customized complete prefix unfolding
- Order for configurations
- Set of configurations
- Equivalence

- Same markings
- The marking was generated by the firing of the same transitions

- Cyclic behavior:
- A transition c is part of cyclic behavior if there is a configuration with two occurrences of c
- Transition c is repeated 1 or more times if it occurs in all runs
- Transition c is repeated 0 or more times if it does not occur in all runs

- It does not guarantee a canonical complete prefix unfolding for equivalent models (pomset-trace equivalence)

- Relations among matched events
- In model 2, there is a state after the execution of task cwhere d and c are mutually exclusive; whereas in model 1, there is a state after the execution of b where c can occur before d, or c can be skipped
- In model 2, there is a state after the execution of task a where c can occur before d, or c can be skipped; whereas in model 1, there is a state after the execution of a where c precedes d

- Mismatching repetitive behavior
- Task b may occur many times in model 2; whereas in model 1, it is not repeated any time
- Task c may occur many times in model 2; whereas in model 1, it is not repeated any time

- Unmatched events
- There is an additional occurrence of task bafter c in model 2
- There is an additional occurrence of task cafter b in model 2

- Technique for a behavioral comparison of process models using AES
- Canonical folding of AES
- Finite representation using Petri net unfoldings
- Characterization of cyclic behavior according to task repetitions

- Categorization of discrepancies for offering a more understandable feedback

- Visualization of discrepancies in the models
- Empirical evaluation of the usefulness of diagnostics using real-world process models
- Test if a more refined feedback can be given by using other models of concurrency

- Consider only common behavior (common labels of tasks)
- One model can have more behavior than other
- Error tolerant graph matching techniques

- Discrepancies
- Mismatching relations among matched events (approximate context)
- Mismatching repetitive behavior
- Unmatched events (approximate context)