Engineering Industry-Ready Anomaly Detection Algorithms

Practical values of anomaly detection algorithms, which are engineered and tested on open data, are often low as their real-world applications are rare. The underlying reason is the lack of consideration for practical needs (i.e., research context). Additionally, the validity of algorithms is a concern due to the absence of a proper research method being followed. This paper reports how we considered the research context and followed the Design Science paradigm to engineer our algorithm. In this way, we can address a real-world application of automatic marine data quality control.