Detecting change in processes using comparative trace clustering

Real-life business processes are complex and show a high degree of variability. Additionally, due to changing conditions and circumstances, these processes continuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Besides changes over time, case data (e.g. treating physician, patient age) also influence how processes are executed. Existing process mining techniques assume processes to be static and therefore are less suited for the analysis of contemporary flexible business processes. This paper presents a novel comparative trace clustering approach that is able to expose changes in behavior. Valuable insights can be gained and process improvements can be made by finding those points in time where behavior changed and the reasons why. Evaluation on real-life event data shows our technique can provide these insights.</p

Finding suitable activity clusters for decomposed process discovery

Event data can be found in any information system and provide the starting point for a range of process mining techniques. The widespread availability of large amounts of event data also creates new challenges. Existing process mining techniques are often unable to handle "big event data" adequately. Decomposed process mining aims to solve this problem by decomposing the process mining problem into many smaller problems which can be solved in less time, using less resources, or even in parallel. Many decomposed process mining techniques have been proposed in literature. Analysis shows that even though the decomposition step takes a relatively small amount of time, it is of key importance in Finding a high-quality process model and for the computation time required to discover the individual parts. Currently there is no way to assess the quality of a decomposition beforehand. We define three quality notions that can be used to assess a decomposition, before using it to discover a model or check conformance with. We then propose a decomposition approach that uses these notions and is able to find a high-quality decomposition in little time

Finding suitable activity clusters for decomposed process discovery

Event data can be found in any information system and provide the starting point for a range of process mining techniques. The widespread availability of large amounts of event data also creates new challenges. Existing process mining techniques are often unable to handle "big event data" adequately. Decomposed process mining aims to solve this problem by decomposing the process mining problem into many smaller problems which can be solved in less time, using less resources, or even in parallel. Many decomposed process mining techniques have been proposed in literature. Analysis shows that even though the decomposition step takes a relatively small amount of time, it is of key importance in finding a high-quality process model and for the computation time required to discover the individual parts. Currently there is no way to assess the quality of a decomposition beforehand. We define three quality notions that can be used to assess a decomposition, before using it to discover a model or check conformance with. We then propose a decomposition approach that uses these notions and is able to find a high-quality decomposition in little time. Keywords: decomposed process mining, decomposed process discovery, distributed computing, event lo

Detecting change in processes using comparative trace clustering

Real-life business processes are complex and show a high degree of variability. Additionally, due to changing conditions and circumstances, these processes continuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Besides changes over time, case data (e.g. treating physician, patient age) also influence how processes are executed. Existing process mining techniques assume processes to be static and therefore are less suited for the analysis of contemporary flexible business processes. This paper presents a novel comparative trace clustering approach that is able to expose changes in behavior. Valuable insights can be gained and process improvements can be made by finding those points in time where behavior changed and the reasons why. Evaluation on real-life event data shows our technique can provide these insights

Detecting changes in process behavior using comparative case clustering

Real-life business processes are complex and often exhibit a high degree of variability. Additionally, due to changing conditions and circumstances, these processes continuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Case data (e.g. treating physician, patient age) also influence how processes are executed. Existing process mining techniques assume processes to be static and therefore are less suited for the analysis of contemporary, flexible business processes. This paper presents a novel comparative case clustering approach that is able to expose changes in behavior. Valuable insights can be gained and process improvements can be made by finding those points in time where behavior changed and the reasons why. Evaluation using both synthetic and real-life event data shows our technique can provide these insights

Using domain knowledge to enhance process mining results

Process discovery algorithms typically aim at discovering process models from event logs. Most algorithms achieve this by solely using an event log, without allowing the domain expert to influence the discovery in any way. However, the user may have certain domain expertise which should be exploited to create better process models. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. First, we present a verification algorithm to verify the presence of certain constraints in a process model. Then, we present three modification algorithms to modify the process model. The outcome of our approach is a Pareto front of process models based on the constraints specified by the domain expert and common quality dimensions of process mining

Enhancing process mining results using domain knowledge

Process discovery algorithms typically aim at discovering process models from event logs. Most discovery algorithms discover the model based on an event log, without allowing the domain expert to influence the discovery approach in any way. However, the user may have certain domain expertise which should be exploited to create a better process model. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. We firstly present a modification algorithm to modify a discovered process model. Furthermore, we present a verification algorithm to verify the presence of user specified constraints in the model. The outcome of our approach is a Pareto front of process models based on the constraints specified by the domain expert and common quality dimensions of process mining

Business process comparison:a methodology and case study

Business processes often exhibit a high degree of variability. Process variants may manifest due to the differences in the nature of clients, heterogeneity in the type of cases, etc. Through the use of process mining techniques, one can benefit from historical event data to extract non-trivial knowledge for improving business process performance. Although some research has been performed on supporting process comparison within the process mining context, applying process comparison in practice is far from trivial. Considering all comparable attributes, for example, leads to an exponential number of possible comparisons. In this paper we introduce a novel methodology for applying process comparison in practice. We successfully applied the methodology in a case study within Xerox Services, where a forms handling process was analyzed and actionable insights were obtained by comparing different process variants using event data.</p