RAMP: Real-Time Anomaly Detection in Scientific Workflows
Jerome Dinal Herath, Changxin Bai, Guanhua Yan, Ping Yang, Shiyong Lu · 2019
Research integrity is crucial to ensuring the trustworthiness of scientific discoveries. This work is aimed at detecting misbehaviors targeting scientific workflows, which are computing paradigms widely used to facilitate scientific collaborations across multiple geographically distributed research sites. We develop a new system called RAMP(Real-Time Aggregated Matrix Profile) for real-time anomaly detection in scientific workflow systems. RAMP builds upon an existing time series data analysis technique called Matrix Profile to detect anomalous distances among subsequences of event streams collected from scientific workflows in an online manner. Using an adaptive uncertainty function, the anomaly detection model is dynamically adjusted to prevent high false alarm rates. RAMP can incorporate user feedback on reported anomalies and modify model parameters to improve anomaly detection accuracy. Our experimental results from applying RAMP to the logs generated by DATAVIEW, a scientific workflow platform, show that RAMP is able to identify a varied range of anomalies with high accuracy for both interleaved and non-interleaved workflow executions in real time.