A Study of Update Methods for BoND-Tree Index on Non-ordered Discrete Vector Data

Abstract

There is an increasing demand from numerous applications such as bioinformatics and cybersecurity to efficiently process various types of queries on datasets in a multidimensional Non-ordered Discrete Data Space (NDDS). An NDDS consists of vectors with values coming from a non-ordered discrete domain for each dimension. The BoND-tree index was recently developed to efficiently process box queries on a large dataset from an NDDS on disk. The original work of the BoND-tree focused on developing the index construction and query algorithms. No work has been reported on exploring efficient and effective up- date strategies for the BoND-tree. In this paper, we study two update methods based on two different strategies for updating the index tree in an NDDS. Our study shows that using the bottom-up update method can provide improved efficiency, comparing to the traditional top-down update method, especially when the number of dimensions for a vector that need to be updated is small. On the other hand, our study also shows that the two update methods have a comparable effectiveness, which indicates that the bottom-up update method is generally more advantageous.