A Study of Quality and Accuracy Trade-offs in Process Mining
نویسندگان
چکیده
The goal of process mining is to extract semantic knowledge from a log consisting of process execution traces for the purposes of process understanding, innovation and improvement. In recent years many algorithms have been proposed to extract process models from logs. The process models describe the ordering relationships between tasks in a process in terms of standard constructs like sequence, parallel, choice and loop. Most algorithms assume that each trace in a log represents a correct execution sequence based on a model. In practice, logs are noisy and algorithms designed for correct logs are not able to handle noisy logs. In this paper we share our key insights from a study of noise in process logs both real and synthetic. Our first finding is that all process logs can be explained by using self-loop and optional structures. Therefore, it is not difficult to build a fully accurate process model for any given log, even logs that contain inaccurate data or noise. Secondly, there is usually not one single, unique process model that can explain a log, i.e. the same log can be explained by a large number of different models. Thirdly, some models are of higher "quality" than others, and given that so many models can explain the same log, it is important to have a metric of quality for a model. Fourth, if a log contains noisy execution traces, a fully accurate process model that explains every trace in the log is not very meaningful because its quality is low. By controlling the use of self-loop and optional structures around tasks and blocks of tasks we can balance the quality and accuracy tradeoff to derive high-quality process models that explains a given percentage of traces in the log. Finally, we describe a novel quality-based algorithm for model extraction in the context of our noisy logs. The results of the experiments with the algorithm on real and synthetic data are reported and analyzed at length.
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ورودعنوان ژورنال:
- INFORMS Journal on Computing
دوره 24 شماره
صفحات -
تاریخ انتشار 2012