Hasso-plattner Institut Fuer Softwaresystemtechnik Gmbh

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Hasso-plattner-institut Fur Softwaresystemtechnik Gmbh

Emanuel Ziegler

Hasso Plattner

Matthieu-Patrick Schapranow

Juchang Lee

Christian Bensberg

Franz Faerber

Lars Fricke

Andreas Tonder

Roland Sedler

A system and a method for analyzing and evaluating genome data in the course of personalized medicine. In-memory technology is provided for processing of genome data and its real-time analysis as a holistic process in the course of personalized medicine. The cloud application helps physicians and researchers to identify the genetic roots for certain tumor types in the treatment of diseases correlating to genomic variants or mutations, such as cancer diseases. The system combines the latest international research results with patient-specific genomic data while eliminating the need for long-lasting manual searches of all dispositions in distributed international research and literature data sources.

System for genomic data processing with an in-memory database system and real-time analysis

A high performance, low-cost, gapped read alignment algorithm is disclosed that produces high quality alignments of a complete human genome in a few minutes. Additionally, the algorithm is more than an order of magnitude faster than previous approaches using a low-cost workstation. The results are obtained via careful algorithm engineering of the seeding based approach. The use of non-hashed seeds in combination with techniques from search engine ranking achieves fast cache-efficient processing. The algorithm can also be efficiently parallelized. Integration into an in-memory database infrastructure (IMDB) leads to low overhead for data management and further analysis.

Efficient genomic read alignment in an in-memory database

Partitioning of a source table of a database to a target table is initiated. Thereafter, a replay table is generated that is populated with triggers for database operations performed on the source table for subsequent replay for the target partitions. Data is later moved (e.g., asynchronously moved, etc.) from the source table to the target table. The database operations are replayed on the target table T subsequent to the moving of the data using the replay table. In addition, the source table is dropped when all of the data has been moved to the target table and there are no operations requiring replay. Related apparatus, systems, techniques and articles are also described.

Database table re-partitioning using trigger-based capture and replay

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Heidelberg, Germany

Emanuel Ziegler