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Efficient Storage and Analysis of Genome Data in Databases
Citation key DorokBTLSM17
Author Sebastian Dorok, Sebastian Breß, Jens Teubner, Horstfried Läpple, Gunter Saake, Volker Markl
Pages 423-442
Year 2017
Journal Proceedings of 17th Conference on Database Systems for Business, Technology, and Web (BTW), LNI 2017
Volume P-265
Abstract Genome-analysis enables researchers to detect mutations within genomes and deduce their consequences. Researchers need reliable analysis platforms to ensure reproducible and comprehensive analysis results. Database systems provide vital support to implement the required sustainable procedures. Nevertheless, they are not used throughout the complete genome-analysis process, because (1) database systems suffer from high storage overhead for genome data and (2) they introduce overhead during domain-specific analysis. To overcome these limitations, we integrate genome-specific compression into database systems using a specialized database schema. Thus, we can reduce the storage overhead to 30%. Moreover, we can exploit genome-data characteristics during query processing allowing us to analyze real-world data sets up to five times faster than specialized analysis tools and eight times faster than a straightforward database approach.
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