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Toward efficient and reliable genome analysis using main-memory database systems

Published: 30 June 2014 Publication History

Abstract

Improvements in DNA sequencing technologies allow to sequence complete human genomes in a short time and at acceptable cost. Hence, the vision of genome analysis as standard procedure to support and improve medical treatment becomes reachable. In this vision paper, we describe important data-management challenges that have to be met to make this vision come true. Besides genome-analysis performance, data-management capabilities such as data provenance and data integrity become increasingly important to enable comprehensible and reliable genome analysis. We argue to meet these challenges by using main-memory database technologies, which combine fast processing capabilities with extensive data-management capabilities. Finally, we discuss possibilities of integrating genome-analysis tasks into DBMSs and derive new research questions.

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Cited By

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  • (2024)Bridging Genomic Data and CRMNew Trends in Marketing and Consumer Science10.4018/979-8-3693-2754-8.ch006(113-134)Online publication date: 14-Jun-2024
  • (2015)The Relational Way To Dam The Flood Of Genome DataProceedings of the 2015 ACM SIGMOD on PhD Symposium10.1145/2744680.2744692(9-13)Online publication date: 31-May-2015

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cover image ACM Other conferences
SSDBM '14: Proceedings of the 26th International Conference on Scientific and Statistical Database Management
June 2014
417 pages
ISBN:9781450327220
DOI:10.1145/2618243
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 30 June 2014

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SSDBM '14 Paper Acceptance Rate 26 of 71 submissions, 37%;
Overall Acceptance Rate 56 of 146 submissions, 38%

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Cited By

View all
  • (2024)Bridging Genomic Data and CRMNew Trends in Marketing and Consumer Science10.4018/979-8-3693-2754-8.ch006(113-134)Online publication date: 14-Jun-2024
  • (2015)The Relational Way To Dam The Flood Of Genome DataProceedings of the 2015 ACM SIGMOD on PhD Symposium10.1145/2744680.2744692(9-13)Online publication date: 31-May-2015

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