When is big data too big? Making data-based models comprehensible
IMAGE: Big Data, published quarterly online with open access options and in print, facilitates and supports the efforts of researchers, analysts, statisticians, business leaders, and policymakers to improve operations, profitability, and… view more Credit: ©Mary Ann Liebert, Inc., publishers New Rochelle, July 11, 2016–Data-driven mathematical modeling is having an enormous impact on the ability to organize and describe very large data sets, and make inferences and predictions about populations and situations based on sampling data. However, as these models become increasingly complex, the ability of users to understand and apply them represents a growing challenge. The article “A Framework for Considering Comprehensibility in Modeling”, which describes this emerging dilemma and a strategy for developing solutions, is published in Big Data, the highly innovative, peer-reviewed journal from Mary Ann Liebert, Inc., publishers.…
Link to Full Article: When is big data too big? Making data-based models comprehensible
When is big data too big? Making data-based models comprehensible
