Discovering Business Area Effects to Process Mining Analysis Using Clustering and Influence Analysis

A common challenge for improving business processes in large organizations is that business people in charge of the operations are lacking a fact-based understanding of the execution details, process variants, and exceptions taking place in business operations. While existing process mining methodologies can discover these details based on event logs, it is challenging to communicate the process mining findings to business people. In this paper, we present a novel methodology for discovering business areas that have a significant effect on the process execution details. Our method uses clustering to group similar cases based on process flow characteristics and then influence analysis for detecting those business areas that correlate most with the discovered clusters. Our analysis serves as a bridge between BPM people and business, people facilitating the knowledge sharing between these groups. We also present an example analysis based on publicly available real-life purchase order process data.Comment: 12 pages. Paper accepted in 23rd International Conference on Business Information Systems (BIS 2020) to be published in a proceedings edition of the Lecture Notes in Business Information Processin

Discovery of frequent episodes in event logs

Lion's share of process mining research focuses on the discovery of end-to-end process models describing the characteristic behavior of observed cases. The notion of a process instance (i.e., the case) plays an important role in process mining. Pattern mining techniques (such as frequent itemset mining, association rule learning, sequence mining, and traditional episode mining) do not consider process instances. An episode is a collection of partially ordered events. In this paper, we present a new technique (and corresponding implementation) that discovers frequently occurring episodes in event logs thereby exploiting the fact that events are associated with cases. Hence, the work can be positioned in-between process mining and pattern mining. Episode discovery has its applications in, amongst others, discovering local patterns in complex processes and conformance checking based on partial orders. We also discover episode rules to predict behavior and discover correlated behaviors in processes. We have developed a ProM plug-in that exploits efficient algorithms for the discovery of frequent episodes and episode rules. Experimental results based on real-life event logs demonstrate the feasibility and usefulness of the approach

From Semantically Abstracted Traces to Process Mining and Process Model Comparison

Process model comparison can be exploited to assess the quality of organizational procedures, to identify non-conformances with respect to given standards, and to highlight critical situations. Sometimes, however, it is difficult to make sense of large and complex process models, while a more abstract view of the process would be sufficient for the comparison task. In this paper, we show how process traces, abstracted on the basis of domain knowledge, can be provided as an input to process mining, and how abstract models (i.e., models mined from abstracted traces) can then be compared and ranked, by adopting a similarity metric able to take into account penalties collected during the abstraction phase. The overall framework has been tested in the field of stroke management, where we were able to rank abstract process models more similarly to the ordering provided by a domain expert, with respect to what could be obtained when working on non-abstract ones. © 2018, Springer Nature Switzerland AG

A Flexible Semantic KPI Measurement System

Linked Data (LD) technology enables integrating information across disparate sources and can be exploited to perform inferencing for deriving added-value knowledge. As such, it can really support performing different kinds of analysis tasks over business process (BP) execution related information. When moving BPs in the cloud, giving rise to Business Process as a Service (BPaaS) concept, the first main challenge is to collect and link, based on a certain structure, information originating from different systems. To this end, two main ontologies are proposed in this paper to enable this structuring: a KPI and a Dependency one. Then, via exploiting these well-connected ontologies, an innovative Key Performance Indicator (KPI) analysis system is built that offers two main analysis capabilities: KPI assessment and drill-down, where the second can enable finding root causes of KPI violations. This system advances the state-of-the-art by exhibiting the capability, through the LD usage, of the flexible construction and assessment of any KPI kind, allowing experts to better explore the possible KPI space

Machine Learning-Based Framework for Log-Lifting in Business Process Mining Applications

Real-life event logs are typically much less structured and more complex than the predefined business activities they refer to. Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and events recorded during process execution. Unfortunately, event logs and process model activities are defined at different levels of granularity. The challenges posed by this discrepancy can be addressed by means of log-lifting. In this work, we develop a machine-learning-based framework aimed at bridging the abstraction level gap between logs and process models. The proposed framework operates in two main phases: log segmentation and machine-learning-based classification. The purpose of the segmentation phase is to identify the potential segment separators in a flow of low-level events, in which each segment corresponds to an unknown high-level activity. For this, we propose a segmentation algorithm based on maximum likelihood with n-gram analysis. In the second phase, event segments are mapped into their corresponding high-level activities using a supervised machine learning technique. Several machine learning classification methods are explored including ANNs, SVMs, and random forest. We demonstrate the applicability of our framework using a real-life event log provided by the SAP company. The results obtained show that a machine learning approach based on the random forest algorithm outperforms the other methods with an accuracy of 96.4%. The testing time was found to be around 0.01s, which makes the algorithm a good candidate for real-time deployment scenarios