Towards Rank-Aware Data Mashups

Abstract

Data mashups are web applications that combine complementary (raw) data pieces from different data services or web data APIs to provide value added information to users. They became so popular over the last few years; their applications are numerous and vary from addressing transient business needs in modern enterprises. Even though data mashups have been the focus of many research works, they still face many challenging issues that have never been explored. The ranking of the data returned by a data mashup is one of the key issues that have received little consideration. Top-k query model ranks the pertinent answers according to a given ranking function and returns only the best results. This paper proposes two algorithms that optimize the evaluation of top-k queries over data mashups. These algorithms are built based on the web data APIs' access methods: bind probe and indexed probe.