During the lifetime of a Business Process changes can be made to the workflow, the required resources, required documents, . . . . Different traces from the same Business Process within a single log file can thus differ substantially due to these changes. We propose a method that is able to detect concept drift in multivariate log files with a dozen attributes. We test our approach on the BPI Challenge 2018 data con- sisting of applications for EU direct payment from farmers in Germany where we use it to detect Concept Drift. In contrast to other methods our algorithm does not require the manual selection of the features used to detect drift. Our method first creates a model that captures the re- lations between attributes and between events of different time steps. This model is then used to score every event and trace. These scores can be used to detect outlying cases and concept drift. Thanks to the decomposability of the score we are able to perform detailed root-cause analysis.
In @cite_3 a concept drift detection method is proposed that uses statistical tests over feature vectors. These feature vectors are based on the order in which activities occur in the workflow. They record for example how many activities always, sometimes, or never occur after a given activity. Other measures can be used to add extra features that may incorporate the other attributes in the log. The method does require manual selection of the exact features to be used in the statistical tests.
@cite_3
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An established method to detect concept drift in data streams is to perform statistical hypothesis testing on the multivariate data in the stream. The statistical theory offers rank-based statistics for this task. However, these statistics depend on a fixed set of characteristics of the underlying distribution. Thus, they work well whenever the change in the underlying distribution affects the properties measured by the statistic, but they perform not very well, if the drift influences the characteristics caught by the test statistic only to a small degree. To address this problem, we show how uniform convergence bounds in learning theory can be adjusted for adaptive concept drift detection. In particular, we present three novel drift detection tests, whose test statistics are dynamically adapted to match the actual data at hand. The first one is based on a rank statistic on density estimates for a binary representation of the data, the second compares average margins of a linear classifier induced by the 1-norm support vector machine (SVM), and the last one is based on the average zero-one, sigmoid or stepwise linear error rate of an SVM classifier. We compare these new approaches with the maximum mean discrepancy method, the StreamKrimp system, and the multivariate Wald–Wolfowitz test. The results indicate that the new methods are able to detect concept drift reliably and that they perform favorably in a precision-recall analysis. Copyright © 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2: 311-327, 2009