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This paper proposes an extension of a design process meta-model that aims at tracing the project design memory.
LEGO (Living Engineering Process) allows building customized process meta-models based on multiple inputs, making an organization more efficient and effective by optimizing resources, time and costs.
Through a deep scientific literature investigation, several applications were indentified, described and discussed, including the role of uncertainty and sensitivity analyses on parameter screening, reliability analysis, robustness design, decision making process, meta-model construction and embedded uncertainty and sensitivity.
We propose a design of a knowledge management system called KnowledgeScope that addresses these problems through (1) an integrated workflow support capability that captures and retrieves knowledge as an organizational process proceeds, i.e., within the context in which it is created and used, and (2) a process meta-model that organizes that knowledge and context in a knowledge repository.
The framework consists of an artifact-centric process meta-model, public view constructing mechanism, and private view and change validating mechanisms, which are specially designed to facilitate the participating organizations to customize their internal operations while ensuring the correctness of the collaborating processes.
In the design time, the authoring tool is driven by the PBL scripting language (learning process meta-model).
The authoring operations of designers make the single learning process meta-model (PBL scripting language) transform to learning process models (PBL scripts).
Notice that the scripting language is also referred to as learning process meta-model (Devedzić 2002; Atkinson and Kuhne 2003; Aßmann et al. 2006) under our application context.
These different level of models include a PBL process meta-model (PBL scripting language), a PBL process model (PBL process script), and a PBL process model instance (PBL module).
Finally, according to the specification of the learning process meta-model (the PBL scripting language), the possibility of interpreting every detailed interactive information of each activity in the learning process model instance is guaranteed.
The following explains the processes: meta-level test data: this part is similar to the part of generating meta-level training data in the validation process.
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