2013 BeyondMyopicInferenceinBigDataP

• (Raman et al., 2013) ⇒ Karthik Raman, Adith Swaminathan, Johannes Gehrke, and Thorsten Joachims. (2013). “Beyond Myopic Inference in Big Data Pipelines.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487588

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Beyond+Myopic+Inference+in+Big+Data+Pipelines
• http://dl.acm.org/citation.cfm?id=2487575.2487588&preflayout=flat#citedby

Quotes

Author Keywords

• Big data pipelines; learning; modular design; probabilistic inference

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 BeyondMyopicInferenceinBigDataP	Thorsten Joachims Johannes Gehrke Karthik Raman Adith Swaminathan			Beyond Myopic Inference in Big Data Pipelines				10.1145/2487575.2487588		2013