Supporting process mining workflows with RapidProM

Process mining is gaining more and more attention both in industry and practice. As such, the number of process mining products is steadily increasing. However, none of these products allow for composing and executing analysis work flows consisting of multiple process mining algorithms. As a result, the analyst needs to perform repetitive process mining tasks manually and scientific process experiments are extremely labor intensive. To this end, we have RapidMiner 5, which allows for the definition and execution of analysis work flows, connected with the process mining framework ProM 6. As such any discovery, conformance, or extension algorithm of ProM can be used within a RapidMiner analysis process thus supporting process mining work flows

A framework for the analysis and comparison of process mining algorithms

Process mining algorithms use event logs to learn and reason about business processes. Although process mining is essentially a machine learning task, little work has been done on systematically analysing algorithms to understand their fundamental properties, such as how much data is needed for confidence in mining. Nor does any rigorous basis exist on which to choose between algorithms and representations, or compare results. We propose a framework for analysing process mining algorithms. Processes are viewed as distributions over traces of activities and mining algorithms as learning these distributions. We use probabilistic automata as a unifying representation to which other representation languages can be converted. To validate the theory we present analyses of the Alpha and Heuristics Miner algorithms under the framework, and two practical applications. We propose a model of noise in process mining and extend the framework to mining from ‘noisy’ event logs. From the probabilities and sub-structures in a model, bounds can be given for the amount of data needed for mining. We also consider mining in non-stationary environments, and a method for recovery of the sequence of changed models over time. We conclude by critically evaluating this framework and suggesting directions for future research

Process-oriented Iterative Multiple Alignment for Medical Process Mining

Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time. These algorithms are heavily dependent on the selected guide-tree metric, often return sum-of-pairs-score-reducing errors that interfere with interpretation, and are computationally intensive for large datasets. To alleviate these issues, we propose process-oriented iterative multiple alignment (PIMA), which contains specialized optimizations to better handle workflow data. We demonstrate that PIMA is a flexible framework capable of achieving better sum-of-pairs score than existing trace alignment algorithms in only O(NL2) time. We applied PIMA to analyzing medical workflow data, showing how iterative alignment can better represent the data and facilitate the extraction of insights from data visualization.Comment: accepted at ICDMW 201