By Topic

Distributed Stream Processing Analysis in High Availability Context

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Marcin Gorawski ; Silesian University of Technology, Poland ; Pawel Marks

Not so long ago data warehouses were used to process data sets loaded periodically during ETL process (extraction, transformation and loading). We could distinguish two kinds of ETL processes: full and incremental. Now we often have to process real-time data and analyse them almost on-the-fly, so the analyses are always up to date. There are many possible applications for real-time data warehouses. In most cases two features are important: delivering data to the warehouse as quick as possible, and not losing any tuple in case of failures. In this paper we propose an architecture for gathering and processing data from geographically distributed data sources. We present theoretical analysis, mathematical model of a data source, some rules of system modules configuration and results of experiments. At the end of the paper our future plans are described briefly

Published in:

Availability, Reliability and Security, 2007. ARES 2007. The Second International Conference on

Date of Conference:

10-13 April 2007