Inverse predictions on continuous models in scientific databases

Abstract

Using continuous models in scientific databases has received an increased attention in the last years. It allows for a more efficient and accurate querying, as well as predictions of the outputs even where no measurements were performed. The most common queries are on how the output looks like for a given input setting. In this paper we study inverse model-based queries on continuous models, where one specifies a desired output and searches for the appropriate input setting, which falls into the reverse engineering category. We propose two possible approaches. The first one is an extension of the inverse regression paradigm. But simply switching the roles of input and output variables poses new challenges, which we overcome by using partial least squares. The second approach formulates the inverse prediction queries as linear optimization problems. We show that even though these two approaches seem completely different, they are closely related, and that the latter is more general. It facilitates the formulation of a wide range of queries, with specifications of fixed values and ranges in both input and output space, enabling the intuitive exploration of the experimental data and understanding the underlying process.