Mashups are situational applications that build data flows to link the contents of multiple Web sources. Often times, ranking the results of a mashup is handled in a materialize-then-sort fashion, since combining multiple data sources usually destroys their original rankings. Moreover, although uncertainty is ubiquitous on the Web, most mashup tools do not reason about or reflect such uncertainty. We introduce MashRank, a mashup tool that treats ranking as a first-class citizen in mashup construction, and allows for rank-joining Web sources with uncertain information. To the best of our knowledge, no current tools allow for similar functionalities. MashRank encapsulates a new probabilistic model reflecting uncertainty in ranking, a set of techniques implemented as pipelined operators in mashup plans, and a probabilistic ranking infrastructure based on Monte-Carlo sampling.

Mashups are situational applications that join multiple sources to better meet the information needs of Web users. Web sources can be huge databases behind query interfaces, which triggers the need of ranking mashup results based on some user preferences. We present MashRank, a mashup authoring and processing system building on concepts from rank-aware processing, probabilistic databases, and information extraction to enable ranked mashups of (unstructured) sources with uncertain ranking attributes. MashRank is based on new semantics, formulations and processing techniques to handle uncertain preference scores, represented as intervals enclosing possible score values. MashRank integrates information extraction with query processing by asynchronously pushing extracted data on-the-fly into pipelined rank-aware query plans, and using ranking early-out requirements to limit extraction cost. To the best of our knowledge, both the technical problems and target applications of MashRank have not been addressed before.