Using Storm for scaleable sequential statistical inference
Citation:
Wilson, S.P., Mai, T., Cogan, P., Bhattacharya, A., Aslett, L., O'Riordain, S. and Roetzer, G., Using Storm for scaleable sequential statistical inference, Proceedings of CompStat 2014, CompStat 2014, Geneva, 17 - 20/08/2014, 2014Download Item:
wilson_etal_compstat2014.pdf (PDF) 1.333Mb
Abstract:
This article describes Storm, an environment for doing streaming data analysis.
Two examples of sequential data analysis | computation of a running summary statistic and
sequential updating of a posterior distribution | are implemented and their performance is
investigated.
Sponsor
Grant Number
Science Foundation Ireland (SFI)
12/RC/2289
Author's Homepage:
http://people.tcd.ie/swilsonDescription:
PUBLISHEDGeneva
Author: WILSON, SIMON
Other Titles:
Proceedings of CompStat 2014CompStat 2014
Type of material:
Conference PaperCollections:
Availability:
Full text availableKeywords:
streaming data, sequential inference, Storm,Subject (TCD):
Smart & Sustainable Planet , Distributed systems , Sequential data analysis , Statistical computingLicences: