BIT: Using Bitmap Index to Speed Up NCBI Taxonomy Computing-Reference-Cited by-同舟云学术

BIT: Using Bitmap Index to Speed Up NCBI Taxonomy Computing

Published:2024-07-10 Issue: Volume:312 Page:1-12
ISSN:
Container-title:Proceedings of the 36th International Conference on Scientific and Statistical Database Management
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Author:

Hu Chuan¹^ORCID,Cai Jiawei¹^ORCID,Zhao Zihao²^ORCID,Shen Zhihong³^ORCID

Affiliation:

1. Computer Network Information Center, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China

2. Huawei Technology Company, China

3. Computer Network Information Center, Chinese Academy of Sciences, China

Funder

National Key R&D Program of China

Informatization Plan of Chinese Academy of Sciences

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3676288.3676294

Reference29 articles.

1. Antônio Camargo. 2022. apcamargo/taxopy: v0.10.1 (v0.10.1). https://doi.org/10.5281/zenodo.6993581

2. S.A. Chamberlain and E. Szöcs. 2013. taxize: taxonomic search and retrieval in R. F1000Research 2 (2013).

3. A survey of bitmap index compression algorithms for Big Data

4. Pierre Colomb, Olivier Raynaud, and Eric Thierry. 2008. Generalized polychotomic encoding: a very short bit-vector encoding of tree hierarchies. In Modelling, Computation and Optimization in Information Systems and Management Sciences: Second International Conference MCO 2008, Metz, France-Luxembourg, September 8-10, 2008. Proceedings. Springer, 77–86.

5. François Deliège and Torben Bach Pedersen. 2010. Position list word aligned hybrid: optimizing space and performance for compressed bitmaps. In Proceedings of the 13th international conference on Extending Database Technology. 228–239.