By Topic

Asymmetric Batch Incremental View Maintenance

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
$31 $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

4 Author(s)
Hao He ; Dept. of Comput. Sci., Duke Univ., Durham, NC, USA ; Junyi Xie ; Jun Yang ; Yu, H.

Incremental view maintenance has found a growing number of applications recently, including data warehousing, continuous query processing, publish/subscribe systems, etc. Batch processing of base table modifications, when applicable, can be much more efficient than processing individual modifications one at a time. In this paper, we tackle the problem of finding the most efficient batch incremental maintenance strategy under a refresh response time constraint; that is, at any point in time, the system, upon request, must be able to bring the view up to date within a specified amount of time. The traditional approach is to process all batched modifications relevant to the view whenever the constraint is violated. However, we observe that there often exists natural asymmetry among different components of the maintenance cost; for example, modifications on one base table might be cheaper to process than those on another base table because of some index. We exploit such asymmetries using an unconventional strategy that selectively processes modifications on some base tables while keeping batching others. We present a series of analytical results leading to the development of practical algorithms that approximate an "oracle algorithm" with perfect knowledge of the future. With experiments on a TPC-R database, we demonstrate that our strategy offers substantial performance gains over traditional deferred view maintenance techniques.

Published in:

Data Engineering, 2005. ICDE 2005. Proceedings. 21st International Conference on

Date of Conference:

05-08 April 2005