Next Step Recommendation and Prediction based on Process Mining in Adaptive Case Management - Huber, Sebastian; Fietta, Marian; Hof, Sebastian (Proceedings of the S-BPM ONE '15, 2015)
Adaptive Case Management (ACM) is a new paradigm that facilitates the coordination of knowledge work through case handling. Current ACM systems, however, lack support of providing sophisticated user guidance for next step recommendations and predictions about the case future. In recent years, process mining research developed approaches to make recommendations and predictions based on event logs readily available in process-aware information systems. This paper builds upon those approaches and integrates them into an existing ACM solution. The research goal is to design and develop a prototype that gives next step recommendations and predictions based on process mining techniques in ACM systems. The models proposed, recommend actions that shorten the case running time, mitigate deadline transgressions, support case goals and have been used in former cases with similar properties. They further give case predictions about the remaining time, possible deadline violations, and whether the current case path supports given case goals. A final evaluation proves that the prototype is indeed capable of making proper recommendations and predictions. In addition, starting points for further improvement are discusse
WiSo2-Import:2015-10-02:640

Process Mining Based Recommender System for Adaptive Case Management - Huber, Sebastian; Fietta, Marian; Hof, Sebastian (Proceedings of the 2015 Annual SRII Global Conference, IEEE, 2015)
WiSo2-Import:2015-10-02:660

Routing in Sparsely-gated Language Models responds to Context - Arnold, Stefan; Fietta, Marian; Yesilbas, Dilara (Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, 2024)
Language Models (LMs) recently incorporate mixture-of-experts layers consisting of a router and a collection of experts to scale up their parameter count given a fixed computational budget. Building on previous efforts indicating that token-expert assignments are predominantly influenced by token identities and positions, we trace routing decisions of similarity-annotated text pairs to evaluate the context sensitivity of learned token-expert assignments. We observe that routing in encoder layers mainly depends on (semantic) associations, but contextual cues provide an additional layer of refinement. Conversely, routing in decoder layers is more variable and markedly less sensitive to context.
CRIS-Team Scopus Importer:2025-04-01