Many domains such as scientific computing and neuroscience require end users to compose heterogeneous computational entities to automate their pro- fessional tasks. However, an issue that frequently hampers such composition is data-mismatches between computational entities. Although, many composition frameworks today provide support for data mismatch resolution through special- purpose data converters, end users still have to put significant effort in dealing with data mismatches, e.g., identifying the available converters and determining which of them meet their QoS expectations. In this paper we present an approach that eliminates this effort by automating the detection and resolution of data mis- matches. Specifically, it uses architectural abstractions to automatically detect different types of data mismatches, model-generation techniques to fix those mis- matches, and utility theory to decide the best fix based on QoS constraints. We illustrate our approach in the neuroscience domain where data-mismatches can be fixed in an efficient manner on the order of few seconds.
['Perla Velasco-Elizondo', 'Vishal Dwivedi', 'David Garlan', 'Bradley R. Schmerl', 'José Maria Fernandes']
Resolving Data Mismatches in End-User Compositions