Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework

Abstract

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.

Backpack Process Model (BPPM): A Process Mining Approach for Curricular Analytics

“…The application of trace clustering techniques, in combination with DFGs, could be useful to reduce the complexity of longer BPPM educational trajectories, decomposing traces into smaller and more understandable backpack trajectories. In this context, hierarchical clustering [54] looks promising for future works. Furthermore, qualitative analysis could expand the understanding of students' beliefs about effort and the nature of intelligence [19] on decisions about course taking, stop out and dropout.…”

Proactive business process mining for end-state prediction using trace features

“…Tariq ar al. [6] proposed a hierarchical clustering based solution to break down large process logs into manageable smaller sub logs. Another technique, trace clustering, is proposed by the De Weerdt et al [7] which divides the event log into manageable homogenous subsets.…”

“…Processes depicting these contradicting behaviours are termed as Spaghatti processes and lasagna processes by authors of [14]. Real-world business processes are variable and contain heterogeneity which makes them Contrail business processes, as proposed by Tariq et al [6]. Most real-world business processes have elements of both Spaghetti and Lasagne processes.…”

An Event-Level Clustering Framework for Process Mining Using Common Sequential Rules

“…Clustering techniques are mostly based on the similarity of traces within an event log [16]. Generally, techniques focused on trace clustering neglect the business perceptive of the process while dealing with real-world logs [21]. Event logs may contain very low level of process details, event-abstraction techniques are developed to group the low-level events into high level events [17], but these techniques tend to ignore the activities which are meaningful for analysis at lower abstraction level.…”

“…-We conducted several experiments to evaluate the proposed technique on a real-world telecommunication data. We also compared the results with other well-known trace clustering techniques from the literature, ActiTraC proposed by Song et al [7] and NoHiC by Tariq et al [21].…”