This work describes a novel end-to-end data ingestion and runtime processing pipeline, which is a core part of a technical solution aiming to monitor frailty indices of patients during and after treatment and improve their quality of life. The focus of this work is on the technical architectural details and the functionalities provided, which have been developed in a manner that are extensible, scalable and fault-tolerant by design. Extensibility refers to both data sources and the exact specification of analysis techniques. Our platform can combine data not only from multiple sensor types but also from electronic health records. Also, the analysis component can process the patient data both individually and in combination with other patients, while exploiting both cloud and edge resources. We have shown concrete examples of advanced analytics and evaluated the scalability of the system, which has been fully prototyped.
Outlier detection in process mining refers to aspects such as infrequent behavior in relation to the underlying business process models or to anomalous latencies of task execution, termed as temporal anomalies. In this work, we focus on the latter form of anomalies and we aim at investigating in depth the behavior of several proximity-based variants, which are shown to outperform simple statistical ones. We investigate multiple distance functions and approaches to establishing the outlierness of traces or individual tasks, and we explain the superiority of our proposals over existing proximity and probability distribution fitting-based techniques yielding up to 2.05X higher F1 score. We also provide guidelines as to which variant to be chosen based on the type of anomalies targeted and the dataset characteristics.
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