In this paper, we introduce BINet, a neural network architecture for real-time multi-perspective anomaly detection in business process event logs. BINet is designed to handle both the control flow and the data perspective of a business process. Additionally, we propose a set of heuristics for setting the threshold of an anomaly detection algorithm automatically. We demonstrate that BINet can be used to detect anomalies in event logs not only on a case level but also on event attribute level. Finally, we demonstrate that a simple set of rules can be used to utilize the output of BINet for anomaly classification. We compare BINet to eight other state-of-the-art anomaly detection algorithms and evaluate their performance on an elaborate data corpus of 29 synthetic and 15 real-life event logs. BINet outperforms all other methods both on the synthetic as well as on the real-life datasets.

The IQ test is an exam most of us are familiar with, regardless of whether we have taken it or not. The test was originally designed by the French psychologist Alfred Binet in the early 1900s. But in the new millennium, is the IQ test still an effective means of measuring general intelligence? According to the general consensus, the answer is "no." An IQ test measures a person's cognitive ability compared to the population at large.