Abstract
The increase in large biomedical data objects stored in long term archives that continuously need to be processed and analyzed requires new storage paradigms. We propose expanding the storage system from only storing biomedical data to directly producing value from the data by executing computational modules - storlets - close to where the data is stored. This paper describes the Storlet Engine, an engine to support computations in secure sandboxes within the storage system. We describe its architecture and security model as well as the programming model for storlets. We experimented with several data sets and storlets including de-identification storlet to de-identify sensitive medical records, image transformation storlet to transform images to sustainable formats, and various medical imaging analytics storlets to study pathology images. We also provide a performance study of the Storlet Engine prototype for OpenStack Swift object storage.
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Acknowledgments
The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement 270000 and under grant agreement 600826.
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Rabinovici-Cohen, S., Henis, E., Marberg, J., Nagin, K. (2015). Storlet Engine for Executing Biomedical Processes Within the Storage System. In: Fournier, F., Mendling, J. (eds) Business Process Management Workshops. BPM 2014. Lecture Notes in Business Information Processing, vol 202. Springer, Cham. https://doi.org/10.1007/978-3-319-15895-2_6
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DOI: https://doi.org/10.1007/978-3-319-15895-2_6
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