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Best Paper Awards for Jens Teubner in projects A2 and C5

Jun 25, 2017 3:53:00 PM


At BTW 2017 in Stuttgart, Jens Teubner received the Best Paper Award for his Paper "Efficient Storage and Analysis of Genome Data in Databases". He developed this work together with the University Magdeburg, Bayer AG, and TU Berlin.

The paper discusses technique to store genome data efficiently in a relational database. This makes the flexibility and performance of modern relational database engines accessible to the analysis of genome data.

At the same day, Stefan Noll, a Master Student of Jens Teubner, received the Best Student Paper Award ath BTW 2017 in Stuttgart. His contribution "Energy Efficiency in Main Memory Databases" reports on the key results of his Master Thesis. The Master Thesis was prepared within the DBIS Group and in the context of the Collaborative Research Center SFB876, Project A2.

His paper shows how the energy efficiency of a database system can be improved by balancing the compute capacity of the system with the available main memory bandwidth. To this end, he proposes to use Dynamic Voltage and Frequency Scaling (DVFS) as well as the selective shutdown of individual cores.

Abstract: "Efficient Storage and Analysis of Genome Data in Databases"
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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