Real-world business processes are dynamic, with event logs that are generally unstructured and contain heterogeneous business classes. Process mining techniques derive useful knowledge from such logs but translating them into simplified and logical segments is crucial. Complexity is increased when dealing with business processes with a large number of events with no outcome labels. Techniques such as trace clustering and event clustering, tend to simplify the complex business logs but the resulting clusters are generally not understandable to the business users as the business aspects of the process are not considered while clustering the process log. In this paper, we provided a multi-stage hierarchical framework for business-logic driven clustering of highly variable process logs with extensively large number of events. Firstly, we introduced a term contrail processes for describing the characteristics of such complex real-world business processes and their logs presenting contrail-like models. Secondly, we proposed an algorithm Novel Hierarchical Clustering (NoHiC) to discover business-logic driven clusters from these contrail processes. For clustering, the raw event log is initially decomposed into high-level business classes, and later feature engineering is performed exclusively based on the business-context features, to support the discovery of meaningful business clusters. We used a hybrid approach which combines rule-based mining technique with a novel form of agglomerative hierarchical clustering for the experiments. A case-study of a CRM process of the UK’s renowned telecommunication firm is presented and the quality of the proposed framework is verified through several measures, such as cluster segregation, classification accuracy, and fitness of the log. We compared NoHiC technique with two trace clustering techniques using two real world process logs. The discovered clusters through NoHiC are found to have improved fitness as compared to the other techniques, and they also hold valuable information about the business context of the process log.

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Business processes in the complex real-world environment are heterogeneous and challenging to monitor for any possible discrepancies. Businesses substantially rely on the efficiency of these processes to maintain the quality of services for their customers and wish to ensure that an executing business process is progressing in the desired manner. Although process mining techniques provide adequate information about the process execution, it is vital to maintain the quality of business processes through an automated process prediction system that analyses and provides constructive feedback for process improvement. Techniques in the literature can predict the future outcome of a business process, but they lack empirical information about the behaviour of an executing process instance as compared to the optimum process model. In this paper, we have proposed an online process prediction framework using features generated through process mining techniques. We used a heuristic miner algorithm to discover the process model and performed conformance analysis to generate features presenting the contextual behaviour of the process instance. We selected highly contributing features to predict the outcome of the real-world business process using several machine learning algorithms. Our experimental results showed high accuracy, recall, and F-measure. We compared our technique with a similar technique from literature and showed that our solution is more reliable in process outcome prediction.

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