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Resolving Data Mismatches in End-User Compositions

Perla Velasco Elizondo, Vishal Dwivedi, David Garlan, Bradley Schmerl and Jose Maria Fernandes.


In Proceedings of the 4th International Symposium on End-User Development, IT University of Copenhagen, Denmark, 10-13 June 2013.

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Abstract
Many domains such as scientific computing and neuroscience require end users to compose heterogeneous computational entities to automate their professional 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 specialpurpose 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 mismatches. Specifically, it uses architectural abstractions to automatically detect different types of data mismatches, model-generation techniques to fix those mismatches, 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.

Keywords: End-user Architecture, SORASCS.  
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