# A Data-Driven Approach to Discovering Process Choreography
## 1 Introduction  
In collaborative networks, the partners work together to create competitive advantages by defining the activities to be carried out by each organization, the business processes to be executed, the roles to be played, the communication channels, and the definition of interoperability at both the process and system levels in order to achieve common business goals [1, 2, 3]; e.g., a supply chain process may involve several organizations [4]. Collaborative networks foster joint problem-solving through resource sharing and the fusion of complementary skills. This collaborative environment enhances organizations' potential to create and acquire knowledge, leading to the innovation of products or services [5]. In this context of collaborative innovation, members of the supply chain plan and implement actions for knowledge sharing and knowledge application to develop new products and services quickly and efficiently, enabling them to maintain and improve their performance in the long term [6]. Furthermore, in Industry 4.0, end-to-end digital integration is required in the supply chain, with a business process design logic that crosses organizational boundaries. These business processes can be defined using the business process model and notion (BPMN) language [7, 8], a standard for graphically representing the logic of the business process and its subsequent automation [9], which not only makes the logic more understandable but also makes it easier to integrate the perspective of the control flow,the subprocesses, the data flows (internal or external), and the resources involved in the processes into a BPMN diagram [10]. Data-driven approaches are characterized by decision-making based on the analysis and interpretation of data, rather than observations, allowing decisions and solutions to be supported by facts [11; 12]. Process mining techniques are implemented through data-driven approaches, making it possible to discover process patterns within event logs and detect and diagnose differences between observed and modeled behavior [10; 13], which helps in decision-making to improve and optimize business processes [14; 15]. In this way, approaches based on process mining techniques have been implemented to verify and enhance business processes. These techniques are characterized by supporting discovery, conformance checking, enhancement, and predictive analytic tasks [16; 17; 18]. In a discovery approach to the business process model, event data generated by the execution of business processes are analyzed, making it possible to identify the logic and behavior of the process from these event data, known as event logs. Process conformance checking consists of evaluating the alignment of the behavior of the actual business process model against the behavior discovered in the event log (generated by the business process itself), to detect any possible deviations. In the process improvement task, various analyses are carried out, considering all the attributes available in the event log and in the real business process model to detect possible bottlenecks, high time consumption in task execution, deviations, and duplication of the execution of tasks by different resources, among others; making it possible to identify opportunities for improvement in the business process. In approaches based on process mining techniques that implement tasks such as predictive process monitoring or trace clustering, an event log preprocessing stage is included, in which the input data must be encoded to feed the prediction or inference algorithm. At this stage, an encoding method is typically implemented to transform complex event data into a numerical or representative feature space [19]. One of the most important methods for this purpose is Doc2 Vec, based on representation learning, developed in natural language processing (NLP) [20]. This learning uses neural network architecture models to automatically learn distributed vector representations of a concept of interest (for example, an activity or a trace) with high quality.