A data skew handling method based on the minimum spatial proximity for parallel spatial database

Yan Zhou; Qing Zhu; Yeting Zhang

doi:10.1117/12.837521

14 October 2009 A data skew handling method based on the minimum spatial proximity for parallel spatial database

Yan Zhou, Qing Zhu, Yeting Zhang

Proceedings Volume 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining; 74921G (2009) https://doi.org/10.1117/12.837521
Event: International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, 2009, Wuhan, China

Abstract

Data skew is one of most important reasons to deteriorate the performance of parallel spatial database. This paper studies the issues of handling data skew in shared nothing parallel spatial database system architecture. A novel data skew handling method is proposed, which fulfill spatial data distribution balancing based on the spatial proximity of data fragments. The minimum spatial proximity is used to be the principle of moving data fragments among different network parallel nodes. Our experimental results show that the proposed data skew handling method can achieve dynamic data load balancing and offer significant improvement for reducing response time of parallel spatial queries.

Citation Download Citation

Yan Zhou, Qing Zhu, and Yeting Zhang "A data skew handling method based on the minimum spatial proximity for parallel spatial database", Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74921G (14 October 2009); https://doi.org/10.1117/12.837521

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available