MRCSI

Author:

Wandelt Sebastian1,Leser Ulf1

Affiliation:

1. Humboldt-Universität zu Berlin, Berlin, Germany

Abstract

Efficiently storing and searching collections of similar strings, such as large populations of genomes or long change histories of documents from Wikis, is a timely and challenging problem. Several recent proposals could drastically reduce space requirements by exploiting the similarity between strings in so-called reference-based compression. However, these indexes are usually not searchable any more, i.e., in these methods search efficiency is sacrificed for storage efficiency. We propose Multi-Reference Compressed Search Indexes (MRCSI) as a framework for efficiently compressing dissimilar string collections. In contrast to previous works which can use only a single reference for compression, MRCSI (a) uses multiple references for achieving increased compression rates, where the reference set need not be specified by the user but is determined automatically, and (b) supports efficient approximate string searching with edit distance constraints. We prove that finding the smallest MRCSI is NP-hard. We then propose three heuristics for computing MRCSIs achieving increasing compression ratios. Compared to state-of-the-art competitors, our methods target an interesting and novel sweet-spot between high compression ratio versus search efficiency.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Efficient Compression and Indexing for Highly Repetitive DNA Sequence Collections;IEEE/ACM Transactions on Computational Biology and Bioinformatics;2021-11-01

2. Parallel compression for large collections of genomes;Concurrency and Computation: Practice and Experience;2021-06-04

3. Sketch distance-based clustering of chromosomes for large genome database compression;BMC Genomics;2019-12

4. Tackling the Challenges of FASTQ Referential Compression;Bioinformatics and Biology Insights;2019-01

5. Compressed Indexes for Repetitive Textual Datasets;Encyclopedia of Big Data Technologies;2019

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