Exploring Deletion Strategies for the BoND-Tree in Multidimensional Non-ordered Discrete Data Spaces

Abstract

Box queries on a dataset in a multidimensional data space are a type of query which specifies a set of allowed values for each dimension. Indexing a dataset in a multidimensional Non-ordered Discrete Data Space (NDDS) for supporting efficient box queries is becoming increasingly important in many application domains such as genome sequence analysis. The BoND-tree was recently introduced as an index structure specifically designed for box queries in an NDDS. Earlier work focused on developing strategies for building an effective BoND-tree to achieve high query performance. Developing efficient and effective techniques for deleting indexed vectors from the BoND-tree remains an open issue. In this paper, we present three deletion algorithms based on different underflow handling strategies in an NDDS. Our study shows that incorporating a new BoND-tree inspired heuristic can provide improved performance compared to the traditional underflow handling heuristics in NDDSs.