ABSTRACT
Predictive monitoring is a key activity in some Process-Aware Information Systems (PAIS) such as information systems for operational management support. Unforeseen circumstances like COVID can introduce changes in human behaviour, processes, or computing resources, which lead the owner of the process or information system to consider whether the quality of the predictions made by the system (e.g., mean time to solution) is still good enough, and if not, which amount of data and how often the system should be trained to maintain the quality of the predictions. To answer these questions, we propose, compare, and evaluate different strategies for selecting the amount of information required to update the predictive model in a context of offline learning. We performed an empirical evaluation using three real-world datasets that span between 2 and 13 years to validate the different strategies which show a significant enhancement in the prediction accuracy with respect to a non-update strategy. © 2022, Springer Nature Switzerland AG.
ABSTRACT
This article presents DemaBot: a low-code solution to create chatbots designed to automate decision-making. Examples of these chatbots range from COVID-19 screening to first-line incident support, amongst others. Using DemaBot, the developer only needs to model the decision that the chatbot will automate using DMN and, optionally, customize the utterances that the chatbot will use to interact with the user. From this information, DemaBot generates automatically the complete set of components that implement a ready-to-use chatbot. Furthermore, it provides help to guide users during the conversation, and optimizes the conversation flow, being able to recognize several parameters in a single turn and asking only for those that are indispensable for the decision. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).