The MACE Approach for Caching Mashups
In recent years, Web 2.0 applications have experienced tremendous growth in popularity. Mashups are a key category of Web 2.0 applications, which empower end-users with a highly personalized mechanism to aggregate and manipulate data from multiple sources distributed across the Web. Surprisingly, there are few studies on the performance and scalability aspects of mashups. In this paper, the authors study caching-based approaches to improve efficiency and scalability of mashups platforms. This paper presents MACE, a caching framework specifically designed for mashups. MACE embodies three major technical contributions. First, the authors propose a mashup structure-aware indexing scheme that is used for locating cached data efficiently. Second, taxonomy awareness into the system is built and provides support for range queries to further improve caching effectiveness. Third, the authors design a dynamic cache placement technique that takes into consideration the benefits and costs of caching at various points within mashups workflows. This paper presents a set of experiments studying the effectiveness of the proposed mechanisms.
caching mashups web 2.0 data b plus trees