Exploring Deletion Strategies for the BoND-Tree in Multidimensional Non-ordered Discrete Data Spaces Ramblin Cherniak Qiang Zhu Department of Computer and Information Science Department of Computer and Information Science The University of Michigan - Dearborn The University of Michigan - Dearborn Dearborn, Michigan 48128 Dearborn, Michigan 48128
[email protected] [email protected] Yarong Gu Sakti Pramanik Department of Computer and Information Science Department of Computer Science and Engineering The University of Michigan - Dearborn Michigan State University Dearborn, Michigan 48128 East Lansing, Michigan 48824
[email protected] [email protected] ABSTRACT Discrete Data Spaces. In Proceedings of IDEAS ’17, Bristol, United Kingdom, Box queries on a dataset in a multidimensional data space are a July 12-14, 2017, 8 pages. type of query which specifies a set of allowed values for each di- https://doi.org/10.1145/3105831.3105840 mension. Indexing a dataset in a multidimensional Non-ordered Discrete Data Space (NDDS) for supporting efficient box queries 1 INTRODUCTION is becoming increasingly important in many application domains such as genome sequence analysis. The BoND-tree was recently in- A multidimensional Non-ordered Discrete Data Space (NDDS) con- troduced as an index structure specifically designed for box queries tains vectors consisting of values from the non-ordered discrete in an NDDS. Earlier work focused on developing strategies for domain/alphabet of each dimension. Examples of non-ordered dis- A G T C building an effective BoND-tree to achieve high query performance. crete data are genomic nucleotide bases (i.e., , , , ), gender, Developing efficient and effective techniques for deleting indexed profession, and color.