Pig Latin is a popular language which is widely used for parallel processing of massive data sets. Currently, subexpressions occurring repeatedly in Pig Latin scripts are executed as many times as they appear, and the current Pig Latin optimizer does not identify reuse opportunities. We present a novel optimization approach aiming at identifying and reusing repeated subexpressions in Pig Latin scripts. Our optimization algorithm, named PigReuse, identifies subexpression merging opportunities, selects the best ones to execute based on a cost function, and reuses their results as needed in order to compute exactly the same output as the original scripts. Our experiments demonstrate the effectiveness of our approach.

With this article, we give an answer to one of the open problems of mashup development that users may face when operating a model-driven mashup tool, namely the lack of modeling expertise . Although commonly considered simple applications, mashups can also be complex software artifacts depending on the number and types of Web resources (the components) they integrate. Mashup tools have undoubtedly simplified mashup development, yet the problem is still generally nontrivial and requires intimate knowledge of the components provided by the mashup tool, its underlying mashup paradigm, and of how to apply such to the integration of the components. This knowledge is generally neither intuitive nor standardized across different mashup tools and the consequent lack of modeling expertise affects both skilled programmers and end-user programmers alike. In this article, we show how to effectively assist the users of mashup tools with contextual, interactive recommendations of composition knowledge in the form of reusable mashup model patterns. We design and study three different recommendation algorithms and describe a pattern weaving approach for the one-click reuse of composition knowledge. We report on the implementation of three pattern recommender plugins for different mashup tools and demonstrate via user studies that recommending and weaving contextual mashup model patterns significantly reduces development times in all three cases.

Despite the recent progresses in end-user development and particularly in mashup application development, developing even simple mashups is still non-trivial and requires intimate knowledge about the functionality of web APIs and services, their interfaces, parameter settings, data mappings, and so on. We aim to assist less skilled developers in composing own mashups by interactively recommending composition knowledge in the form of modeling patterns and fostering knowledge reuse. Our prototype system demonstrates our idea of interactive recommendation and automated pattern weaving, which involves recommending relevant composition patterns to the users during development, and once selected, applying automatically the changes as suggested in the selected pattern to the mashup model under development. The experimental evaluation of our prototype implementation demonstrates that even complex composition patterns can be efficiently stored, queried and weaved into the model under development in browser-based mashup tools.