Presented at the 57th Annual Meeting of the Human Factors and Ergonomics Society (HFES), San Diego, California, (October 2013)
To address issues in enterprise-level systems, operations research (OR) analysts need to be able to understand, codify, and communicate various aspects of problems, such as relevant operating conditions and their relationships. Models are a natural way to capture that information, but they must be understandable to a variety of stakeholders. At the same time, the large amount of available information, such as event history and weather data, can easily overload analysts. To help analysts cope with data overload, it is useful for models to be accessible to computational tools that can provide data processing and visualization capabilities. To support both of those goals simultaneously, we describe an approach that supports the elicitation of qualitative insight from operations researchers and other relevant stakeholders and also provides avenues for computer software to perform semantic labeling and quantitative data processing. This approach directly supports an iterative OR process that satisfies the needs of multiple stakeholder communities, enabling initial qualitative relationships and hypotheses to be further investigated and justified with data-driven conclusions. Building on our previous experiences in knowledge acquisition and quantitative analysis, this paper outlines a new integrated workflow and a collection of graphical representation concepts for operations research and similar domains.
1 Charles River Analytics Inc.
3 Roth Cognitive Engineering
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