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The potential risk of convert causal matrix to petri net?
Hi,
I saw the definition of causal matrix in genetic mining. But it's not like petri net or other modelling language, it doesn't talk about the properties like soundness or valid sequences, etc. Does it means it can be converted to petri net safely without mistakes? If not, is it highly similar to causal net or any other modelling languages, I can refer to? I hope to understand all potential problems before this conversion (causal matrix to petri net).
I saw the definition of causal matrix in genetic mining. But it's not like petri net or other modelling language, it doesn't talk about the properties like soundness or valid sequences, etc. Does it means it can be converted to petri net safely without mistakes? If not, is it highly similar to causal net or any other modelling languages, I can refer to? I hope to understand all potential problems before this conversion (causal matrix to petri net).
Comments
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Hi Boboane,
Good question, but you're not quite right I'm afraid.
Most process discovery algorithms work in 2 phases. In the first phase they inspect the event log and build an intermediary data structure. This can be a causal matrix (how many times is a followed by b), directly follows graph, etc. etc. In the second phase this intermediary representation is transtlated to a process model (Petri net, Heuristics net, Causal net, process tree, etc.). So a causal net is simply a registry of what behavior has been seen. Translating this to a process model is actually the key of a discovery algorithm.
I hope my explanation is clear, otherwise please ask.
Joos Buijs
Senior Data Scientist and process mining expert at APG (Dutch pension fund executor).
Previously Assistant Professor in Process Mining at Eindhoven University of Technology -
Thanks very much for your help JBuijs.
JBuijs said:Hi Boboane,
Good question, but you're not quite right I'm afraid.
Most process discovery algorithms work in 2 phases. In the first phase they inspect the event log and build an intermediary data structure. This can be a causal matrix (how many times is a followed by b), directly follows graph, etc. etc. In the second phase this intermediary representation is transtlated to a process model (Petri net, Heuristics net, Causal net, process tree, etc.). So a causal net is simply a registry of what behavior has been seen. Translating this to a process model is actually the key of a discovery algorithm.
I hope my explanation is clear, otherwise please ask.
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