Project Overview

Related Course Project

Extension of STLT

Project Members

Sponsors

Publications

Project Overview

A lot of recent work has focussed on bulk loading of data into multidimensional index structures in order to efficiently construct such structures for large datasets. Previous work on bulk loading data focussed at building index structures it from scratch, while the problem of bulk insertions into existing index structures has been largely overlooked. In this paper, we address this new problem with particular focus on R-trees. We propose a new technique, which as opposed to the current technique of inserting data one by one, bulk-inserts entire new datasets into an active R-tree. This technique is called STLT (for Small-Tree-Large-Tree). Our experimental results indicate that the quality of the resulting tree constructed by STLT in terms of query performance is acceptable and in many cases even preferable over that created by the traditional tree insertion approach.

Some previous work (including the Code Base) is done by students in UMich.

Related Course Project

Progress Report

for BackGround and Some Initial Ideas, click here

Final Course Report

for Conclusion on the First Implementation of STLT, click here

EXtension of STLT

Project Members

Advisor

Elke Rundensteiner

Graduate Students

Li Chen

Rupesh Choubey

Ying-Huey Fua

Sponsors

Publications

1. Li Chen, Rupesh Choubey, and Rundensteiner, E. A., "STLT: Bulk insertion into R-trees", Technical Report WPI-CS-TR, 1998.
2. Li Chen, Rupesh Choubey, and Rundensteiner, E. A., "Bulk insertion into R-trees", submitted to 6th ACM Workshop on Geographic Information Systems (ACM-GIS'98), Washington DC, November 1998.