Abstract
Genome data exists in many different formats in distributed data sources. Due to the many possible uses, various tools have been created for analysis and visualization. The huge size of the data sets combined with limited network capacity makes it very time-consuming to use cloud applications. In this contribution, I propose to transfer application logic to the data instead. This can be achieved by making the apps work independently from how and where the data is stored. In-memory databases enable this through on-the-fly data transformations and specialized query execution plans for distributed data.
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Liedke, F. (2014). Exchanging Medical Knowledge. In: Plattner, H., Schapranow, MP. (eds) High-Performance In-Memory Genome Data Analysis. In-Memory Data Management Research. Springer, Cham. https://doi.org/10.1007/978-3-319-03035-7_4
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DOI: https://doi.org/10.1007/978-3-319-03035-7_4
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