In the mid-nineteenth century, the Chicago Board of Trade set uniform standards for the quality of wheat which led to standardized grading of agricultural commodities, not just in the city but throughout the grain-raising Midwest. This abstraction of grain into data facilitated interoperability between farmers, grain elevator operators, and traders, at a time when information processing systems were ledgers and chits on paper. This seemingly simple innovation revolutionized the food system and established Chicago as a major financial center. The data abstraction of food as simple commodities, however, may no longer be sufficient to address pressing national/global challenges in food security due to climate change, as well as public health challenges such as obesity and malnutrition.
How can the food system be enhanced to design products that are flavorful, nutritious, and fun while reducing food waste and achieving sustainable life cycles in terms of water and energy?
Optimizing the food system—which includes producing, transporting, trading, storing, processing, packaging, wholesaling, retailing, consuming, and disposing of food—requires a data ontology that preserves interoperability among the distinct industries that engage in these activities, yet captures much more detailed and expressive data at the level of individual agricultural fields, individual culinary recipes, and individual food waste digesters. To utilize the power of such localized, big data, there is an additional need to automatically cast information across industries into the standardized ontology. Moreover, privacy-preservation guarantee requirements will be needed to make valuable food data assets accessible. The ontological semantics for data privacy compliance may vary across stages in the food pipeline, but the machine learning and stochastic optimization algorithms that determine the privacy-utility tradeoff of data analytics would span across.
In support of this broad goal, this proposed Research Topic will curate research papers on the use of data in the food pipeline, from farm to fork to food waste management.
In the mid-nineteenth century, the Chicago Board of Trade set uniform standards for the quality of wheat which led to standardized grading of agricultural commodities, not just in the city but throughout the grain-raising Midwest. This abstraction of grain into data facilitated interoperability between farmers, grain elevator operators, and traders, at a time when information processing systems were ledgers and chits on paper. This seemingly simple innovation revolutionized the food system and established Chicago as a major financial center. The data abstraction of food as simple commodities, however, may no longer be sufficient to address pressing national/global challenges in food security due to climate change, as well as public health challenges such as obesity and malnutrition.
How can the food system be enhanced to design products that are flavorful, nutritious, and fun while reducing food waste and achieving sustainable life cycles in terms of water and energy?
Optimizing the food system—which includes producing, transporting, trading, storing, processing, packaging, wholesaling, retailing, consuming, and disposing of food—requires a data ontology that preserves interoperability among the distinct industries that engage in these activities, yet captures much more detailed and expressive data at the level of individual agricultural fields, individual culinary recipes, and individual food waste digesters. To utilize the power of such localized, big data, there is an additional need to automatically cast information across industries into the standardized ontology. Moreover, privacy-preservation guarantee requirements will be needed to make valuable food data assets accessible. The ontological semantics for data privacy compliance may vary across stages in the food pipeline, but the machine learning and stochastic optimization algorithms that determine the privacy-utility tradeoff of data analytics would span across.
In support of this broad goal, this proposed Research Topic will curate research papers on the use of data in the food pipeline, from farm to fork to food waste management.
