Scientific hypothesis database

Abstract

New instruments and techniques used in capturing scientific data are exponentially increasing the volume of data consumed by in-silico research, usually referred to as data deluge. Once captured, scientific data goes through a cleaning workflow before getting ready to analysis that will eventually confirm the scientists hypothesis. The whole process is, nevertheless, complex and takes the focus of the scientist attention away from his/her research and towards solving the complexity associated with managing computing products. Moreover, as the research evolves, products of the exploration become an important source of provenance information and reuse. Based on these observations, we claim that in-silico experiments must be supported by a holistic hypothesis data model. The latter offers a data perspective for scientific hypothesis specification, and a declarative approach for expressing and running simulations. By confronting simulation results with phenomenon observations scientific hypothesis quantitative validation is achieved, guiding parameters tuning and, eventually, hypothesis evolution. This paper presents the hypothesis data model, shows how to run experiments by transforming declarative simulation expressions into a query evaluation plan and discusses the first implementation prototype using the query engine QEF, developed at the DEXL laboratory. A neuroscience scientific model illustrates the applicability of the data model.