An adaptive work distribution mechanism based on reinforcement learning

Work distribution, as an integral part of business process management, is more widely acknowledged by its’ importance for process-aware information systems. Although there are emerging a wide variety of mechanisms to support work distribution, they less concern performance considerations and cannot balance work distribution requirements and process performance within the change of process conditions. This paper presents an adaptive work distribution mechanism based on reinforcement learning. It considers process performance goals, and then can learn, reason suitable work distribution policies within the change of process conditions. Also, learning-based simulation experiment for addressing work distribution problems of business process management is introduced. The experiment results show that our mechanism outperforms reasonable heuristic or hand-coded approaches to satisfy process performance goals and is feasible to improve current state of business process management

Analyzing conformance to clinical protocols involving advanced synchronizations

Clinical protocols are a popular instrument to document how clinicians are expected to behave under specific conditions. Protocols are typically based on internationally peer reviewed clinical guidelines as well as on hospital-local agreements. Existing techniques for monitoring protocol adherence only support protocol descriptions involving simple sequences and local decision rules. As care and cure processes are becoming increasingly complex, the need for more advanced techniques naturally emerges. In this paper we present a novel approach to defining and monitoring complex clinical protocols. By using BPMN to document protocols we enable the concise specification of protocols that involve multiple stakeholders that operate in parallel and under uncertainty. Uncertainty relates to the fact that protocols may involve complex loops and choices. While this specification style was becoming increasingly popular in the literature and practice of hospital management and operations management in general, corresponding conformance analysis techniques were still lacking. This paper contributes the first such technique and evaluate it on a complex compliance pattern from the cardiology domain

Towards Multi-perspective Conformance Checking with Aggregation Operations

Conformance checking techniques are widely adopted to validate process executions against a set of constraints describing the expected behavior. However, most approaches adopt a crisp evaluation of deviations, with the result that small violations are considered at the same level of significant ones. Furthermore, in the presence of multiple data constraints the overall deviation severity is assessed by summing up each single deviation. This approach easily leads to misleading diagnostics; furthermore, it does not take into account user’s needs, that are likely to differ depending on the context of the analysis. We propose a novel methodology based on the use of aggregation functions, to assess the level of deviation severity for a set of constraints, and to customize the tolerance to deviations of multiple constraints