On the use of Process Mining and Machine Learning to support decision making in systems design

Research on process mining and machine learning techniques has recently received a significant amount of attention by product development and management communities. Indeed, these techniques allow both an automatic process and activity discovery and thus are high added value services that help reusing knowledge to support decision-making. This paper proposes a double layer framework aiming to identify the most significant process patterns to be executed depending on the design context. Simultaneously, it proposes the most significant parameters for each activity of the considered process pattern. The framework is applied on a specific design example and is partially implemented.FUI GONTRAN

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Prehistoric Baseline reveals Substantial Decline of Oyster Reef Condition in a Gulf of Mexico Conservation Priority Area

The Gulf of Mexico (GoM) is home to the world\u27s largest remaining wild oyster fisheries, but baseline surveys needed to assess habitat condition are recent and may represent an already-shifted reference state. Here, we use prehistoric oysters from archaeological middens to show that oyster size, an indicator of habitat function and population resilience, declined prior to the earliest assessments of reef condition in an area of the GoM previously considered pristine. Stable isotope sclerochronlogy reveals extirpation of colossal oysters occurred through truncated life history and slowed growth. More broadly, our study suggests that management strategies affected by shifting baselines may overestimate resilience and perpetuate practices that risk irreversible decline