Artificial Digital Photo Copier Event Log

Artificial event log for a digital photo copier. Used in a demo paper accepted at the CAiSE 2011 Forum. This paper is also to accepted for publication in the CAiSE 2011 Forum post-proceedings, which is to be published as a volume in Springer's LNBIP series (see http://www.springer.com/series/7911)

A case study on analyzing inter-organizational business processes from EDI messages using physical activity mining

In order to achieve their goals, organizations collaborate with business partners. Such collaborations represent enactments of inter-organizational business processes and may be supported through the exchange of Electronic Data Interchange (EDI) messages (e.g., electronic purchase orders, invoices etc.). For gaining insights on such processes, recently two distinct approaches for enabling the application of process mining techniques on inter-organizational business processes based on the interchanged EDI messages have been proposed: (i) Message Flow Mining (MFM) and (ii) Physical Activity Mining (PAM). In this paper, we present a case study in which we apply the PAM methodology on a real-world EDI data set obtained from a German manufacturer of consumer goods. Our results demonstrate potential insights that can be gained from applying process mining techniques in the context of inter-organizational business processes

Process diagnostics using trace alignment : opportunities, issues, and challenges

Business processes leave trails in a variety of data sources (e.g., audit trails, databases, and transaction logs). Hence, every process instance can be described by a trace, i.e., a sequence of events. Process mining techniques are able to extract knowledge from such traces and provide a welcome extension to the repertoire of business process analysis techniques. Recently, process mining techniques have been adopted in various commercial BPM systems (e.g., BPM|one, Futura Reflect, ARIS PPM, Fujitsu Interstage, Businesscape, Iontas PDF, and QPR PA). Unfortunately, traditional process discovery algorithms have problems dealing with less structured processes. The resulting models are difficult to comprehend or even misleading. Therefore, we propose a new approach based on trace alignment. The goal is to align traces in such a way that event logs can be explored easily. Trace alignment can be used to explore the process in the early stages of analysis and to answer specific questions in later stages of analysis. Hence, it complements existing process mining techniques focusing on discovery and conformance checking. The proposed techniques have been implemented as plugins in the ProM framework. We report the results of trace alignment on one synthetic and two real-life event logs, and show that trace alignment has significant promise in process diagnostic efforts

Process mining applied to the BPI Challenge 2012 : divide and conquer while discerning resources

A real-life event log, taken from a Dutch financial institute, is analyzed using state-of-the-art process mining techniques. The log contains events related to loan/overdraft applications of customers. We propose a hierarchical decomposition of the log into homogenous subsets of cases based on characteristics such as the final decision, offer, and suspicion of fraud. These subsets are used to uncover interesting insights. The event log in its entirety and the homogeneous subsets are analyzed using various process mining techniques. In this paper, we present results related to (a) resource perspective and their influence on execution/turnaround times of activities, (b) control-flow perspective, and (c) process diagnostics. A dedicated ProM1 plug-in developed for this challenge allows for a comprehensive analysis of the resource perspective. For the analysis of control-flow and process diagnostics, we use recent, but pre-existing, ProM plug-ins. As the evaluation shows, our mix of techniques is able to uncover many interesting findings and could be used to improve the underlying loan/overdraft application handling process

Process mining applied to the BPI Challenge 2012 : divide and conquer while discerning resources

A real-life event log, taken from a Dutch financial institute, is analyzed using state-of-the-art process mining techniques. The log contains events related to loan/overdraft applications of customers. We propose a hierarchical decomposition of the log into homogenous subsets of cases based on characteristics such as the final decision, offer, and suspicion of fraud. These subsets are used to uncover interesting insights. The event log in its entirety and the homogeneous subsets are analyzed using various process mining techniques. In this paper, we present results related to (a) resource perspective and their influence on execution/turnaround times of activities, (b) control-flow perspective, and (c) process diagnostics. A dedicated ProM1 plug-in developed for this challenge allows for a comprehensive analysis of the resource perspective. For the analysis of control-flow and process diagnostics, we use recent, but pre-existing, ProM plug-ins. As the evaluation shows, our mix of techniques is able to uncover many interesting findings and could be used to improve the underlying loan/overdraft application handling process

Analysis of patient treatment procedures: The BPI Challenge case study

A real-life event log, taken from a Dutch Academic Hospital, is analyzed using process mining techniques. The log contains events related to treatment and diagnosis steps for patients diagnosed with cancer. Given the heterogeneous nature of these cases, we first demonstrate that it is possible to create more homogeneous subsets of cases (e.g. patients having a particular type of cancer that need to be treated urgently). Such preprocessing is crucial given the variation and variability found in the event log. The discovered homogeneous subsets are analyzed using state-of-the-art process mining approaches. In this paper, we report on the findings discovered using enhanced fuzzy mining and trace alignment. A dedicated preprocessing ProM3 plug-in was developed for this challenge. The analysis was done using recent, but pre-existing, ProM plug-ins. As the evaluation shows, this approach is able to uncover many interesting findings and could be used to improve the underlying care processes

Context aware trace clustering : towards improving process mining results

Process Mining refers to the extraction of process models from event logs. Real-life processes tend to be less structured and more flexible. Traditional process mining algorithms have problems dealing with such unstructured processes and generate spaghetti-like process models that are hard to comprehend. An approach to overcome this is to cluster process instances (a process instance is manifested as a trace and an event log corresponds to a multi-set of traces) such that each of the resulting clusters correspond to a coherent set of process instances that can be adequately represented by a process model. In this paper, we propose a context aware approach to trace clustering based on generic edit distance. It is well known that the generic edit distance framework is highly sensitive to the costs of edit operations. We define an automated approach to derive the costs of edit operations. The method proposed in this paper outperforms contemporary approaches to trace clustering in process mining. We evaluate the goodness of the formed clusters using established fitness and comprehensibility metrics defined in the context of process mining. The proposed approach is able to generate clusters such that the process models mined from the clustered traces show a high degree of fitness and comprehensibility when compared to contemporary approaches