Integrating computer log files for process mining: a genetic algorithm inspired technique

Process mining techniques are applied to single computer log files. But many processes are supported by different software tools and are by consequence recorded into multiple log files. Therefore it would be interesting to find a way to automatically combine such a set of log files for one process. In this paper we describe a technique for merging log files based on a genetic algorithm. We show with a generated test case that this technique works and we give an extended overview of which research is needed to optimise and validate this technique

Desire lines in big data : using event data for process discovery and conformance checking

Recently, the Task Force on Process Mining released the Process Mining Manifesto. The manifesto is supported by 53 organizations and 77 process mining experts contributed to it. The active contributions from end-users, tool vendors, consultants, analysts, and researchers illustrate the growing relevance of process mining as a bridge between data mining and business process modeling. This paper summarizes the manifesto and explains why process mining is a highly relevant, but also very challenging, research area. This way we hope to stimulate the broader IS (Information Systems) and KM (Knowledge Management) communities to look at process-centric knowledge discovery. This paper summarizes the manifesto and is based on a paper with the same title that appeared in the December 2011 issue of SIGKDD Explorations (Volume 13, Issue 2)

Extracting event data from databases to unleash process mining

Increasingly organizations are using process mining to understand the way that operational processes are executed. Process mining can be used to systematically drive innovation in a digitalized world. Next to the automated discovery of the real underlying process, there are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards "better" processes. Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. However, process mining stands or falls with the availability of event logs. Existing techniques assume that events are clearly defined and refer to precisely one case (i.e. process instance) and one activity (i.e., step in the process). Although there are systems that directly generate such event logs (e.g., BPM/WFM systems), most information systems do not record events explicitly. Cases and activities only exist implicitly. However, when creating or using process models "raw data" need to be linked to cases and activities. This paper uses a novel perspective to conceptualize a database view on event data. Starting from a class model and corresponding object models it is shown that events correspond to the creation, deletion, or modification of objects and relations. The key idea is that events leave footprints by changing the underlying database. Based on this an approach is described that scopes, binds, and classifies data to create "flat" event logs that can be analyzed using traditional process-mining techniques

Finding errors in the design of a workflow process : a Petri-net-based approach

Workflow management systems facilitate the everyday operation of business processes by taking care of the logistic control of work. In contrast to traditional information systems, they attempt to support frequent changes of the workflows at hand. Therefore, the need for analysis methods to verify the correctness of workflows is becoming more prominent. In this paper we present a method based on Petri nets. This analysis method exploits the structure of the Petri net to find potential errors in the design of the workflow. Moreover, the analysis method allows for the compositional verification of workfIows

Mediating between modeled and observed behavior : the quest for the "right" process

Operational processes leave trails in the information systems supporting them. Such event data are the starting point for process mining – an emerging scienti¿c discipline relating modeled and observed behavior. Whereas an event log describes example behavior of the underlying process, a process model aims to describe an abstraction of the same process. Models may be descriptive or normative. Descriptive models aim to describe the underlying process and are used for discussion, performance analysis, obtaining insights, and prediction. Normative models describe the desired behavior and are used for work¿ow management, system con¿guration, auditing, compliance management, and conformance checking. Differences between modeled and observed behavior may point to undesirable deviations or inadequate models. In this paper, we discuss challenges related to ¿nding the "right" process, i.e., the process model that describes the real underlying process or a process that behaves as desired