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
CITATION STYLE
Rabinovici-Cohen, S., Henis, E., Marberg, J., & Nagin, K. (2015). Storlet engine for executing biomedical processes within the storage system. In Lecture Notes in Business Information Processing (Vol. 202, pp. 59–71). Springer Verlag. https://doi.org/10.1007/978-3-319-15895-2_6
Mendeley helps you to discover research relevant for your work.