Abooks and the AIM project

During the last decades, learning has once again become a key topic. However, this time, not only for students and professors but also in political and economic contexts. One reason for this is that a high level of education and skills of nations, organizations, and individuals are considered both necessary and crucially competitive advantages in the present knowledge society and the globalized market. Therefore, obtaining a quality education is fundamental for all of us in today\u27s competitive business world. In particular, adult learning within the maritime sector has been important for the success of this industry for ages. The question now is how to streamline and facilitate the learning process for the learners, the lecturers, the authors, and the learning institutions. TERP has taken on the challenge of improving this learning process by introducing Abooks, electronic textbooks based on principles of pedagogy (the science of learning), and andragogy (the science of learning focusing on adults) that adapt to the learner through artificial intelligence. Abooks also introduces the opportunity of utilizing immersive techniques. This is being developed in the AIM project; Adapting to the Individual through Machine learning, a research project led by the research department in TERP in collaboration with the University of Stavanger and the Norwegian Computing Center

BINet: Multi-perspective Business Process Anomaly Classification

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

PREDICTIVE BUSINESS PROCESS MONITORINGWITH CONTEXT INFORMATION FROM DOCUMENTS

Predictive business process monitoring deals with predicting a process’s future behavior or the value of process-related performance indicators based on process event data. A variety of prototypical predictive business process monitoring techniques has been proposed by researchers in order to help process participants performing business processes better. In practical settings, these techniques have a low predictive quality that is often close to random, so that predictive business process monitoring applications are rare in practice. The inclusion of process-context data has been discussed as a way to improve the predictive quality. Existing approaches have considered only structured data as context. In this paper, we argue that process-related unstructured documents are also a promising source for extracting process-context data. Accordingly, this research-in-progress paper outlines a design-science research process for creating a predictive business process monitoring technique that utilizes context data from process-related documents to predict a process instance’s next activity more accurately