# Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework
## 1 Introduction
We used process modelling techniques, such as fuzzy mining and heuristic mining, for analysing the fitness criteria of an event log [7]. Our framework is generalised for real-world business processes showing contrail-like model, helping the organisations to understand the way their PAISs are executing documented business processes. To present the usefulness of the proposed framework, we are using heterogeneous business process data from BT, one of the leading telecom firms in the UK. The business process selected for this study contains more than 1300 unique cases, with an average of 250 events each. Events present hybrid data types such as integer data (Case_ID, duration of case execution), nominal data (customer's answer to specific questions related to services), and text data (customer's inquiry about the problem). Our proposed framework provides solution for several existing issues in the business processes domain. Firstly, the real-world business processes are naturally complex and require non-traditional techniques to understand them. Single business process often contains several sub-business processes and comprises of data gathered from heterogeneous sources of information. The traditional machine learning techniques tend to solve the problem of decomposing the data into several small segments, but the biggest challenge in using such techniques is that the business perspective that is reflected inside the data is no longer considered. Secondly, process mining deals with such complex scenarios in process mining domain, the process discovery phase is helpful in analysing the log and converting the event data into a process model. This process model reflects the control-flows, business logic and other domain specific information. Process mining techniques tend to provide understanding of the business process through discovery phase, but they lack in capturing the evolving nature of real-world business processes.Lastly, business users gets the insights about the business processes through statistical techniques, but these techniques lack the business-context in their solution. In the proposed multi-stage framework, the first stage is to simplify the contrail processes by identifying the fundamental high-level business classes within the event log. One way of doing this is to use a segment of the log with similar attributes and classifying this as a subset [1]. Another simplification of the complex process log is to identify the common paths in the process model and make subsets based on the flow of process [8]. Trace clustering in this way is not always beneficial as it may classify less traversed paths in the model as outliers, also it is difficult to communicate the results to business users as they are always interested to know the reason behind clusters in the data. In contrast, our proposed work is focused primarily on the business aspect of the process instances. Initially, high-level business classes within the process log are segregated such that each class is handled separately. Information about business classes is generally available within the process log or can be identified through domain experts. As an example, consider an organisation's human resource process log which contains events for the employee hiring processes. High-level business classes in the process log can be extracted through the segregation of data such as job applicant's previous experience, qualification, job type, or hiring department. In a second stage, each of these business classes is considered for in-depth divisive hierarchical clustering. The clustering of the cases is purely based on business aspects identified from the process attributes that have a direct impact on the business objectives. At the leaf-nodes of the clustering tree, cases in the identified clusters are highly associated with each other in terms of the business logic they adopt to achieve the business goals.