Uncertainty propagation in data processing systems
Uncertainty propagation in data processing systems Manousakis et al., SoCC’18
When I’m writing an edition of The Morning Paper, I often imagine a conversation with a hypothetical reader sat in a coffee shop somewhere at the start of their day. There are three levels of takeaway from today’s paper choice:
- If you’re downing a quick espresso, then it’s good to know that uncertainty can creep into our data in lots of different ways, and if you compute with those uncertain values as if they were precise, errors can compound quickly leading to incorrect results or false confidence.
- If you’re savouring a cortado, then you might also want to dip into the techniques we can use to propagate uncertainty through a computation.
- If you’re lingering over a latte, then the UP (Uncertainty Propagation) framework additionally shows how to integrate these techniques into a dataflow framework.
We implement this framework in a system called UP-MapReduce, and use it to modify ten applications, including AI/ML, image processing, and trend analysis applications to process uncertain data. Our evaluation shows that UP-MapReduce propagates uncertainties with high accuracy and, in many cases, low performance overheads.
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Uncertainty can arise from a number of Continue reading


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