X2H

X2H transforms XML-based biological datasets into scalable HBase representations and maps XML query models to MapReduce to enable efficient large-scale storage and querying of structured biological data.


Key Features:

  • Efficient Data Storage: Constructs HBase tables from XML-based biological data collections to leverage HBase scalability and performance for large datasets.
  • Advanced Query Capabilities: Provides a formal transformation of the XML query model into the MapReduce query model to improve query performance over traditional declarative XML query languages.
  • Performance Evaluation: Demonstrates substantial performance advantages through evaluation of query performance on existing XML-based biological databases.

Scientific Applications:

  • Large-scale XML biological data management: Supports scalable storage and access for bioinformatics resources encoded in XML.
  • Scalable query execution on XML databases: Enables efficient execution and benchmarking of queries on structured biological datasets.

Methodology:

Converts XML-based biological data into HBase tables and applies a formal transformation of the XML query model into the MapReduce query model for distributed query execution.

Topics

Details

License:
Unlicense
Maturity:
Mature
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Linux, Windows, Mac
Programming Languages:
Java
Added:
8/9/2019
Last Updated:
6/16/2020

Operations

Publications

Liu J, Liu Q, Zhang L, Su S, Liu Y. Enabling Massive XML-Based Biological Data Management in HBase. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2020;17(6):1994-2004. doi:10.1109/tcbb.2019.2915811. PMID:31094692.

PMID: 31094692
Funding: - National Key R&D Program of China: 2017YFC1200200, 2017YFC1200205, 2018YFC1603800, 2018YFC1603802 - National Natural Science Foundation of China: 61602130, 61872115 - China Postdoctoral Science Foundation: 2015M581449, 2016T90294 - Heilongjiang Postdoctoral Fund: LBH-Z14089 - Natural Science Foundation of Heilongjiang Province: QC2015067 - Fundamental Research Funds for the Central Universities: HIT.NSRIF.2017036 - Shanghai Municipal Science and Technology Major Project: 2017SHZDZX01

Documentation

Links