Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Efficient bulk-loading of gridfiles

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.

The purchase and pricing options are temporarily unavailable. Please try again later.
2 Author(s)
Leutenegger, S.T. ; Dept. of Math. & Comput. Sci., Denver Univ., CO, USA ; Nicol, D.M.

This paper considers the problem of bulk-loading large data sets for the gridfile multiattribute indexing technique. We propose a rectilinear partitioning algorithm that heuristically seeks to minimize the size of the gridfile needed to ensure no bucket overflows. Empirical studies on both synthetic data sets and on data sets drawn from computational fluid dynamics applications demonstrate that our algorithm is very efficient, and is able to handle large data sets. In addition, we present an algorithm for bulk-loading data sets too large to fit in main memory. Utilizing a sort of the entire data set it creates a gridfile without incurring any overflows

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:9 ,  Issue: 3 )