# A Data-Driven Approach to Discovering Process Choreography
## 1 Introduction
Doc2 Vec is an architecture for computing continuous vector representations of words from large datasets with high dimensionality. In the process mining domain, several approaches based on representation learning techniques have been presented to significantly improve the performance of the inference algorithm. In [21], the authors proposed several activity-level models, traces, models, and logs to deal with the high dimensionality of real-life event logs and to generate a distributed representation that can be used in different process mining tasks. In [22], the authors presented a case-level solution that uses word embeddings for business process data to better encode process instances. For their part, ref. [23] expounded an approach for conformance verification based on vector representations of each activity/task present in the model and the event log. Therefore, the vectors generated by Doc2 Vec can be used to find similarities between traces, allowing for the quick analysis of large event logs by expressing words in the vector-space model and considering the context when learning through the co-occurrence of activities. Recent studies have proposed solutions for different process mining tasks applied in intra-organizational business processes [24; 25; 26; 27]. However, when process mining solutions are implemented in inter-organizational business processes (IOBP), aspects such as the process's privacy and autonomy; data with different levels of granularity; and event data stored in other sources, formats, and distribution form must be considered. Therefore, managing independently generated event logs requires methodologies and algorithms to process, align, and merge the event logs generated by process-oriented information systems [28]. Importantly, events need to be correlated across organizational boundaries. Then, by implementing process mining techniques, the tasks of discovery, monitoring, compliance, and improvement of IOBPs, which have yet to be studied to date, can be carried out. Furthermore, the analysis can be extended to discover and verify the process choreography, which represents the formalization of interactions through messages from the participants in an inter-organizational collaboration [29]. In this sense, automatic analysis of the historical information recorded from the execution of the business processes of the participating organizations can help to find relationships within the IOBP. The above can be achieved through data-driven and process model-level analysis. At the structured data level, the organizations participating in the IOBP are responsible for selecting and structuring the data from their information systems and consequently choosing the appropriate level of abstraction and the point of view of the data. At the level of process models, the business process flow of the participating organizations is analyzed, in search of patterns that can complement the analysis of structured data, to obtain sufficient information for identifying collaboration patterns between organizations and discovering the IOBP model. Different approaches are available in the state of the art that partially address analyzing and discovering process choreography, focusing on the analysis of the information contained in event logs [30; 31; 32], in business process models [33; 34; 35; 36; 37], document electronics, and information related to the business process [38]. Therefore, this paper proposes a data-driven methodology supported by semi-automatic methods that enable the discovery of the IOBP model and the process choreography in a collaborative environment. The relationships between the organizations participating in the business process are identified, labeled, and defined using a method based on the Doc2 Vec algorithm and by calculating the cosine similarity measure between events to identify possible message-type tasks and their task subtype (send/receive), for which a set of definitions are specified to formalize the relationships, as well as a group of rules for assigning the message task subtype.