Beyond Myopic Inference In Big Data Pipelines

Big Data Pipelines decompose complex analyses of large data sets into a series of simpler tasks, with independently tuned components for each task. This modular setup allows re-use of components across several different pipelines. However, the interaction of independently tuned pipeline components yields poor end-to-end performance as errors introduced by one component cascade through the whole pipeline, affecting overall accuracy. We propose a novel model for reasoning across components of Big Data Pipelines in a probabilistically well-founded manner. Our key idea is to view the interaction of components as dependencies on an underlying graphical model. Different message passing schemes on this graphical model provide various inference algorithms to trade-off end-to-end performance and computational cost. We instantiate our framework with an efficient beam search algorithm, and demonstrate its efficiency on two Big Data Pipelines: parsing and relation extraction.