Method and apparatus with neural network to measure process-sequences similarities and training thereof

A training method performed by a computing device, the training method comprising:

• based on a first training parameter, embedding each of first processes comprised in a first process-sequence as respective first embedding vectors comprising a vector corresponding to a corresponding process type and at least one vector corresponding to at least one process parameter according to the corresponding process type;
• based on the first training parameter, embedding second processes comprised in a second process-sequence as respective second embedding vectors comprising a vector corresponding to a corresponding process type and at least one vector corresponding to at least one process parameter according to a corresponding process type;
• based on a second training parameter, mapping first embedding vectors respectively corresponding to the first processes to second embedding vectors respectively corresponding to the second processes;
• based on a result of the mapping, determining a similarity score indicating similarity between the first process-sequence and the second process-sequence;
• and
• based on a loss of a multivariate distribution model for the determined similarity score, training the first training parameter and the second training parameter.

An apparatus comprising:

• one or more processors;
• a memory storing instructions configured to cause the one or more processors to:
• based on a first training parameter, perform first embedding by embedding respective first processes comprised in a first process-sequence as a first embedding vector corresponding to a corresponding process type and at least one vector corresponding to at least one process parameter according to the corresponding process type;
• based on the first training parameter, perform second embedding by embedding second processes comprised in a second process-sequence as respective second embedding vectors corresponding to a corresponding process type and corresponding to at least one process parameter according to a corresponding process type;
• based on a second training parameter, map first embedding vectors corresponding to the first processes to respective second embedding vectors corresponding to the second processes;
• and
• based on a result of the mapping, determine a similarity score of similarity between the first process-sequence and the second process-sequence.