Enhancing Scalability and Performance of Mashups Through Merging and Operator Reordering

Recently, mashups are gaining tremendous popularity as an important Web 2.0 application. Mashups provide end-users with an opportunity to create personalized Web services which aggregate and manipulate data from multiple diverse sources distributed across the Web. However, this increase in personalization also results in new scalability and performance challenges. Surprisingly, there are very few studies on the performance aspect of mashups. In this paper, we propose two novel techniques to enhance the scalability and performance of mashup platforms. The first is an efficient mashup merging scheme that avoids duplicate computations and unnecessary data retrievals by detecting common operator sequences in different mashups and executing them together. Second, we propose a canonical form-based mashup reordering scheme that not only transforms individual mashups to their most efficient forms but also increases the effectiveness of mashup merging. This paper also reports a number of experiments studying the benefits and costs of the proposed techniques.