Polymorphic type framework for scientific workflows with relational data model

Scientific workflow systems provide languages for representing complex scientific processes as decompositions into lower level tasks, down to the level of atomic, executable units. To support data analysis activities, a wide variety of such languages represent data transformation and processing operations as task nodes within a workflow. Adding data type information to the task inputs and outputs allows workflow authors to perform type checking at design time, search for compatible nodes in public component repositories and define specifications of abstract workflows. Introducing support for strict data typing simplifies the implementation of a workflow system in addressing these issues, but at the expense of losing flexibility. We address this challenge by developing a data typing framework for scientific workflow systems that supports polymorphic data types that specify the minimal type constraints on node inputs and outputs. We focus on applications that use a relational data model and provide a polymorphic type formula composition algorithm for workflow nodes and fragments. The techniques introduced are validated by applying the inference engine prototype to an adverse drug reaction study performed with the relational algebra subset of the Discovery Net workflow system.