Abstract
While column-oriented in-memory databases have been primarily designed to support fast OLAP queries and business intelligence applications, their analytical performance makes them a promising platform for data mining tasks found in life sciences. One such system is the HANA database, SAP’s in-memory data management solution. In this contribution, we show how HANA meets some inherent requirements of data mining in life sciences. Furthermore, we conducted a case study in the area of proteomics research. As part of this study, we implemented a proteomics analysis pipeline in HANA. We also implemented a flexible data analysis toolbox that can be used by life sciences researchers to easily design and evaluate their analysis models.
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Boese, JH. et al. (2013). Data Mining in Life Sciences: A Case Study on SAPs In-Memory Computing Engine. In: Castellanos, M., Dayal, U., Rundensteiner, E.A. (eds) Enabling Real-Time Business Intelligence. BIRTE 2012. Lecture Notes in Business Information Processing, vol 154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39872-8_2
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DOI: https://doi.org/10.1007/978-3-642-39872-8_2
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