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

12 pages•Published: March 9, 2020

Ramblin Cherniak, Qiang Zhu and Sakti Pramanik

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.

Keyphrases: algorithm, database, multidimensional indexing, non ordered discrete data space, update

In: Gordon Lee and Ying Jin (editors). Proceedings of 35th International Conference on Computers and Their Applications, vol 69, pages 122-133.

Links:	https://easychair.org/publications/paper/C4lK
	https://doi.org/10.29007/3zq4

BibTeX entry

@inproceedings{CATA2020:Study_Update_Methods_BoND,
  author    = {Ramblin Cherniak and Qiang Zhu and Sakti Pramanik},
  title     = {A Study of Update Methods for BoND-Tree Index on Non-ordered Discrete Vector Data},
  booktitle = {Proceedings of 35th International Conference on Computers and Their Applications},
  editor    = {Gordon Lee and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {69},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/C4lK},
  doi       = {10.29007/3zq4},
  pages     = {122-133},
  year      = {2020}}

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