Abstract
Several currently ongoing research efforts aim to combine Markov Chain Monte Carlo (MCMC) with database management systems. The goal is to scale up the management of uncertain data in contexts where only MCMC is known to be applicable or where the range and flexibility of MCMC provides a compelling proposition for powerful and interesting systems. This talk surveys recent work in this area and identifies open research challenges.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Koch, C. (2010). Markov Chain Monte Carlo and Databases. In: Deshpande, A., Hunter, A. (eds) Scalable Uncertainty Management. SUM 2010. Lecture Notes in Computer Science(), vol 6379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15951-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-15951-0_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15950-3
Online ISBN: 978-3-642-15951-0
eBook Packages: Computer ScienceComputer Science (R0)