Data Interlocking: Coupling Analytics to the Data

´Big data´ analytics can be defined by the requirement for flexible, high throughput computational analysis methods applied to large, heterogeneous datasets. We propose an architectural approach to ´big data´ challenges in which the movement of data is minimized, and analysis methods are implemented on the data as portable services. We term this approach ´data interlocking´. We demonstrate the feasibility of this approach through a domain specific implementation of a data interlocking architecture, in which an on-demand computational workbench provides portable high-throughput analysis methods to large genomic datasets on cloud infrastructure.