By Topic

Building Reliable Data Pipelines for Managing Community Data Using Scientific Workflows

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

5 Author(s)
Yogesh Simmhan ; Sci. Group, Microsoft Res., Los Angeles, CA, USA ; Catharine van Ingen ; Alex Szalay ; Roger Barga
more authors

The growing amount of scientific data from sensors and field observations is posing a challenge to ¿data valets¿ responsible for managing them in data repositories. These repositories built on commodity clusters need to reliably ingest data continuously and ensure its availability to a wide user community. Workflows provide several benefits to modeling data-intensive science applications and many of these benefits can help manage the data ingest pipelines too. But using workflows is not panacea in itself and data valets need to consider several issues when designing workflows that behave reliably on fault prone hardware while retaining the consistency of the scientific data. In this paper, we propose workflow designs for reliable data ingest in a distributed environment and identify workflow framework features to support resilience. We illustrate these using the data pipeline for the Pan-STARRS repository, one of the largest digital surveys that accumulates 100TB of data annually to support 300 astronomers.

Published in:

e-Science, 2009. e-Science '09. Fifth IEEE International Conference on

Date of Conference:

9-11 Dec. 2009