Rediscovering workflow models from event-based data using little thumb

By A. Weijters, Wil M.P. van der Aalst. Integr. Comput. Aided Eng., 2003

592 citations recorded by the source corpus.

Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we propose a technique for rediscovering workflow models. This technique uses workflow logs to discover the workflow process as it is actually being executed. The workflow log contains information about events taking place. We assume that these events are totally ordered and each event refers to one task being executed for a single case. This information can easily be extracted from transactional information systems (e.g., Enterprise Resource Planning systems such as SAP and Baan). The rediscovering technique proposed in this paper can deal with noise and can also be used to validate workflow processes by uncovering and measuring the discrepancies between prescriptive models and actual process executions.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/56e8c83f186150da6cfba959d7b4e6a10e5aa618

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Citations in the corpus

Conformance checking of processes based on monitoring real behavior (2008, unknown): “Another example is the process mining approach presented in [37], which aims at the discovery of a WF-net that (i) potentially generates all event sequences appearing in the execution log (i.”
Genetic Process Mining (2005, methodology): “The authors of this paper have been involved in different variants of the so-called α-algorithm [4, 18].”
Process Mining and Verification of Properties: An Approach Based on Temporal Logic (2005, unknown): “In [35] a heuristic approach using rather simple metrics is used to construct so-called “dependency/frequency tables” and “dependency/frequency graphs”.”
The ProM Framework: A New Era in Process Mining Tool Support (2005, methodology): “The ProM framework subsumes process mining tools like EMiT [1], Little Thumb [17] and MinSon [2].”
Fuzzy Mining - Adaptive Process Simplification Based on Multi-perspective Metrics (2007, unknown): “While parting with some characteristics of traditional process mining techniques, such as absolute precision and describing the complete behavior, it is obvious that the approach described in this paper is based on previous work in this domain, most specifically control-flow mining algorithms [2, 14”
Business Process Management: A Comprehensive Survey (2013, unknown): “Since the mid-nineties several groups have been working on techniques for process discovery [34, 36, 44, 68, 72, 80, 81, 219].”
Process mining: a two-step approach to balance between underfitting and overfitting (2008, methodology): “It appears that some of the ideas presented in [40] can be combined with other approaches, including the one presented in this paper.”
Application of Process Mining in Healthcare - A Case Study in a Dutch Hospital (2008, methodology): “In this paper, we use the heuristic mining algorithm, since it can deal with noise and exceptions, and enables users to focus on the main process flow instead of on every detail of the behavior appearing in the process log [12].”
Process Mining (2005, methodology): “In contrast to the previous papers, our work [24, 36] is characterized by the focus on workflow processes with concurrent behavior (rather than adding ad-hoc mechanisms to capture parallelism).”
Discovering Social Networks from Event Logs (2005, unknown): “In (Weijters and Aalst 2003) a heuristic approach using rather simple metrics is used to construct so-called “dependency/frequency tables” and “dependency/frequency graphs”.”
Workflow mining: A survey of issues and approaches (2003, unknown): “The idea of process mining is not new [8,11,15–17,24–29,42–44,53–57,61–63].”
Process Mining (2016, background): “mance checking approaches use a similar representation [4, 12, 66, 183, 184].”
Genetic process mining: an experimental evaluation (2007, methodology): “This heuristic way of building an individual is based on the work presented in [60].”
Business process mining: An industrial application (2007, methodology): “In the last five years, several mining tools have been developed at Eindhoven University of Technology, e.g., EMiT [9], Thumb [ 15 ], and MiSoN [10].”
Trace Clustering in Process Mining (2008, background): “, process models) only from event logs [1, 2, 4, 9, 12].”
Discovering expressive process models by clustering log traces (2006, unknown): “, techniques in [5], [6], [7] are able to produce models that focus on the main behavior as reported in the log by properly dealing with noise).”
Process Discovery using Integer Linear Programming (2009, unknown): “Instead, we propose to guide the search for solutions (i.e. for places) by metrics from the ﬁeld of process discovery [5,7,15,21].”