A new approach to performance optimization of mashups via data flow refactoring

Abstract

Mashup tools allow end users graphically build complex mashups using pipes to connect web data sources into a data flow. Because end users are of poor technical expertise, the designed data flows may be inefficient. This paper targets on enhancing the performance of mashups via automatically refactoring the structure of its data flows. First a set of operational semantics features are selected for annotating the operators in data flows and refactoring rules are defined to generate all candidate semantics equivalent data flows. Then a heuristic algorithm is described for accurately searching the data flow of minimal execution time by constructing a partially ordered set of data flows based on their cost estimation. This approach is applicable to general mashup data flows without knowing complete operational semantics of their operators and the efficiency improvement is demonstrated by experiments.