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

Dorok, Sebastian; Breß, Sebastian; Läpple, Horstfried; Saake, Gunter

doi:10.1145/2618243.2618276

Cited by 2 publications

(2 citation statements)

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“…Relational DBSs provide capabilities such as data security, privacy, and integration that improve genome-data management and analysis [9]. Existing work mainly uses disk-based relational DBSs to deal with genome data.…”

Section: Related Workmentioning

confidence: 99%

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The Relational Way To Dam The Flood Of Genome Data

Dorok

2015

Proceedings of the 2015 ACM SIGMOD on PhD Symposium

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Mutations in genomes can indicate a predisposition for diseases such as cancer or cardiovascular disorder. Genome analysis is an established procedure to determine mutations and deduce their impact on living organisms. The first step in genome analysis is DNA sequencing that makes the biochemically stored hereditary information in DNA digitally readable. The cost and time to sequence a whole genome decreases rapidly and leads to an increase of available raw genome data that must be stored and integrated to be analyzed. Damming this flood of genome data requires efficient and effective analysis as well as data management solutions. State-of-the-art in genome analysis are flat-file-based storage and analysis solutions. Consequently, every analysis application is responsible to manage data on its own, which leads to implementation and process overhead.Database systems have already shown their ability to reduce data management overhead for analysis applications in various domains. However, current approaches using relational database systems for genome-data management lack scalable performance on increasing amounts of genome data. In this thesis, we investigate the capabilities of relational main-memory database systems to store and query genome data efficiently, while enabling flexible data access.

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Section: Related Workmentioning

confidence: 99%

“…To overcome scalability problems of disk-based DBSs, mainmemory relational DBSs seem to be a promising approach [9]. Schapranow et al provide an approach that enables fast analysis of genome data using state-of-the-art tools running on a main-memory DBS platform [21].…”

Section: Related Workmentioning

confidence: 99%

The Relational Way To Dam The Flood Of Genome Data

Dorok

2015

Proceedings of the 2015 ACM SIGMOD on PhD Symposium

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Bridging Genomic Data and CRM

Behare,

Mahajan,

Behare

et al. 2024

Advances in Marketing, Customer Relationship Management, and E-Services

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The combination of genetic data and CRM systems has pushed customer engagement into a new world, totally transforming the traditional marketing strategies. This chapter explores the impact of the relationship between genomics and CRM together on understanding consumer behaviour, personalization, and targeted marketing. This chapter starts with the overview of using genomic data for understanding marketing. The discussion then progresses to the core concepts of CRM and the importance of the need of integrating CRM strategies with genomic data. Real world case studies are demonstrated highlighting the benefits of integrating genomic data and CRM. Emphasising the importance of handling the data with responsibility and maintaining the customer privacy, this chapter also addresses the arising challenges, explores the enhancements in the technological infrastructure required to facilitate the integration, and predicts the emerging trends. It also provides practical tips to find ways to adapt to this changing landscape and set the foundation for a customer centric future driven by genomic data.

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

Cited by 2 publications

References 21 publications

The Relational Way To Dam The Flood Of Genome Data

The Relational Way To Dam The Flood Of Genome Data

Bridging Genomic Data and CRM

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