Resolving Data Mismatches in End-User Compositions

Abstract

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.

Foundations and Tools for End-User Architecting

“…c. Analysis: Similar to dynamic network analysis, the properties of the style elements are used for designing various domain-specific analyses for the brain imaging domain. An example is data mismatch analysis to support the detection of data mismatches in the neuroscience compositions and to suggest repairs that can resolve these mismatches based on an end user's quality of service requirements [56]. d. Execution support: Workflows are compiled into BPEL scripts, which are executed on a service-oriented platform, identical to SORASCS, providing the similar feedback and debugging facilities.…”