# A Data-Driven Approach to Discovering Process Choreography
## 2 Related Work  
In [39], a technique to discover collaboration models from intra-organizational event logs was proposed. The structure of the event log was extended to support interaction data between participants by adding attributes to contain the message name, message identifier, participant role, and type of communication between participants. Interactions between participants are identified through an event data analysis in the event log, determining the correlation between the messages exchanged. Subsequently, the intra-organizational models discovered with the information from the interaction of messages are combined, which enables the generation of an inter-organizational business process model aligned with the BPMN language. Intra-organizational business process models are discovered for each participant in the collaboration by applying algorithms available in the state of the art. Similarly, ref. [40] presented a process mining approach to discover inter-organizationalbusiness processes and process choreography from an extended event log. This log requires information about the participants and the messages exchanged between the participants, to discover a model of the inter-organizational process represented by the BPMN language. The extended event log includes information required for the inter-organizational process model and process choreography. For example, the participant attribute identifies the participant that executes the event, and an attribute contains the type of event; in the case of message-type events, the information of the participant who receives the message is required. A fundamental stage in this proposal is extracting all message-type events and the information related to the message: the participant who sends or receives it. With this extra event log, a model of the process with the message interactions between the participants involved in the collaboration is discovered. This model is used to build process choreography and inter-organizational models in conjunction with the intra-organizational process models discovered for each participant. Our proposal takes a different approach from the studies mentioned above. It does not require the extension of the event log or adding information about the messages and resources exchanged between the collaboration participants. Instead, our method is based on a unique set of methods and formal rules. These tools allow for the identification of potential message tasks and the determination of the task's subtype, which in turn defines the message's meaning (send/receive). On the other hand, ref. [41] proposed a process mining technique to merge intra-organizational event logs and discover an inter-organizational process model represented by a directly-follows graph. This approach is characterized by only using the common elements of an event log: case ID, timestamps, and activity. Furthermore, it is based on the premise that two activities of different organizations occur consecutively with a very short time difference, for which several time thresholds are defined. Therefore, adjacent activities with the minimum time difference should be interconnected and extracted, since they belong to the same trace within an inter-organizational event log, forming the sequence of the activities of the merged event log, ordered by the timestamp value. Each extracted activity pair will be identified in this log by concatenation with the original case IDs. The rest of the events of the same trace (which were not extracted) of each participant are embedded in the trace according to the timestamp value, with which the trace is constructed with all its events. This procedure is executed until no adjacent activities are identified in the event logs of each collaboration participant. In our case, the relationship between message tasks is determined by a cosine similarity measure that ensures that two tasks (from different participants) are close and possibly related.