Data wrangling, or preparing data for use and analysis, consumes far more resources than the actual use and analysis of the data once prepared, in large part because data wrangling is a largely manual process requiring expertise and judgment on the part of the wrangler. This project seeks to develop methods for reducing the effort needed in wrangling, and for automating or partially automating wrangling knowledge to reduce the expertise needed. These methods would apply concepts from database theory, automated planning, and economic decision theory to formalize measures of readiness of the data for different operations, uses, and tasks, and to construct wrangling plans that guarantee the specified quality of the data as required by the user.
Sponsored by the Laboratory for Analytic Sciences
LAS aims to bring together a multi-disciplinary group of academic, industry, and government researchers, analysts and managers together to re-engineer the intelligence analysis process to promote predictive analysis. LAS will do this by conducting both classified and unclassified research in a variety of areas of research. The research done in this area will serve as the foundation for mission effects and integrated back into the enterprise.
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