Campus Units
Computer Science, Electrical and Computer Engineering
Document Type
Article
Publication Date
2017
Journal or Book Title
arXiv
Abstract
A current challenge for data management systems is to support the construction and maintenance of machine learning models over data that is large, multi-dimensional, and evolving. While systems that could support these tasks are emerging, the need to scale to distributed, streaming data requires new models and algorithms. In this setting, as well as computational scalability and model accuracy, we also need to minimize the amount of communication between distributed processors, which is the chief component of latency. We study Bayesian networks, the workhorse of graphical models, and present a communication-efficient method for continuously learning and maintaining a Bayesian network model over data that is arriving as a distributed stream partitioned across multiple processors. We show a strategy for maintaining model parameters that leads to an exponential reduction in communication when compared with baseline approaches to maintain the exact MLE (maximum likelihood estimation). Meanwhile, our strategy provides similar prediction errors for the target distribution and for classification tasks.
Language
en
File Format
application/pdf
Recommended Citation
Zhang, Yu; Tirthapura, Srikanta; and Cormode, Graham, "Learning Graphical Models from a Distributed Stream" (2017). Electrical and Computer Engineering Publications. 175.
https://lib.dr.iastate.edu/ece_pubs/175
Comments
This is a manuscript of the article Zhang, Yu, Srikanta Tirthapura, and Graham Cormode. "Learning Graphical Models from a Distributed Stream." arXiv preprint arXiv:1710.02103 (2017). Posted with permission.