Generation of process models in domains with unstructured data

A system, comprising:

• a domain storing a master data schema of the domain and a transaction data schema of the domain;
• and
• a computing server comprising a memory and at least one processor, the memory comprising instructions executable by the at least one processor, the instructions, when executed, cause the at least one processor to:
• receive the master data schema and the transaction data schema;
• identify named entities in unstructured data of the domain based on the master data schema and the transaction data schema, at least one of the identified named entity corresponding to values stored in the master data schema or the transaction data schema;
• identify activities associated with the named entities, at least one of the identified activities extracted from the unstructured data;
• label the activities with timestamps to generate an activity time sequence that arranges labeled activities in accordance with the timestamps, the activity time sequence generated by:
• representing the named entities as vectors based on relationships among the named entities referenced in the unstructured data;
• inputting the activities with the timestamps that are associated with the vectors into a machine learning model;
• and
• generating, by the machine learning model, the activity time sequence;
• generate one or more event sequences related to a target process associated with the domain, at least one of the event sequences generated from the activity time sequence;
• combine the one or more of event sequences to generate a process model representing the target process, the process model comprising data representing a directed and branched graph, at least a branch in the graph corresponding to the at least one of the event sequences generated from the activity time sequence;
• and
• identify a bottleneck in the target process based on the data representing the directed and branched graph.

A non-transitory computer readable medium for storing computer code comprising instructions, when executed by at least one processor, cause the at least one processor to:

• receive a master data schema of a domain and a transaction data schema of the domain;
• identify named entities in unstructured data of the domain based on the master data schema and the transaction data schema, at least one of the identified named entity corresponding to values stored in the master data schema or the transaction data schema;
• identify activities associated with the named entities, at least one of the identified activities extracted from the unstructured data;
• label the activities with timestamps to generate an activity time sequence that arranges labeled activities in accordance with the timestamps, the activity time sequence generated by:
• representing the named entities as vectors based on relationships among the named entities referenced in the unstructured data;
• inputting the activities with the timestamps that are associated with the vectors into a machine learning model;
• and
• generating, by the machine learning model, the activity time sequence;
• generate one or more event sequences related to a target process associated with the domain, at least one of the event sequences generated from the activity time sequence;
• combine the one or more event sequences to generate a process model representing the target process, the process model comprising data representing a directed and branched graph, at least a branch in the graph corresponding to the at least one of the event sequences generated from the activity time sequence;
• and
• identify a bottleneck in the target process based on the data representing the directed and branched graph.