15 citations · 25 across the 3 of their papers we have counts for
6 papers
Extracting and Pre-Processing Event Logs
Dirk Fahland
Event data is the basis for all process mining analysis. Most process mining techniques assume their input to be an event log. However, event data is rarely recorded in an event lo…
Striking a new balance in accuracy and simplicity with the Probabilistic Inductive Miner
Dennis Brons, Roeland Scheepens, Dirk Fahland
Numerous process discovery techniques exist for generating process models that describe recorded executions of business processes. The models are meant to generalize executions int…
Process Discovery Using Graph Neural Networks
Dominique Sommers, Vlado Menkovski, Dirk Fahland
Automatically discovering a process model from an event log is the prime problem in process mining. This task is so far approached as an unsupervised learning problem through graph…
Multi-Dimensional Event Data in Graph Databases
Stefan Esser, Dirk Fahland
Process event data is usually stored either in a sequential process event log or in a relational database. While the sequential, single-dimensional nature of event logs aids queryi…
Scalable Alignment of Process Models and Event Logs: An Approach Based on Automata and S-Components
Daniel Reißner, Abel Armas-Cervantes, Raffaele Conforti +3
Given a model of the expected behavior of a business process and an event log recording its observed behavior, the problem of business process conformance checking is that of ident…
Artifact Lifecycle Discovery
Viara Popova, Dirk Fahland, Marlon Dumas
Artifact-centric modeling is a promising approach for modeling business processes based on the so-called business artifacts - key entities driving the company's operations and whos…